Overview

Dataset statistics

Number of variables57
Number of observations372915
Missing cells12062488
Missing cells (%)56.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory173.1 MiB
Average record size in memory486.7 B

Variable types

Numeric13
Categorical34
Boolean10

Alerts

Forks has constant value "True"Constant
Turbocharged has constant value "True"Constant
Blade_Extension has constant value "True"Constant
Pushblock has constant value "True"Constant
Scarifier has constant value "True"Constant
Coupler_System has constant value "True"Constant
Grouser_Tracks has constant value "True"Constant
Backhoe_Mounting has constant value "True"Constant
fiModelDesc has a high cardinality: 4639 distinct valuesHigh cardinality
fiBaseModel has a high cardinality: 1797 distinct valuesHigh cardinality
fiSecondaryDesc has a high cardinality: 163 distinct valuesHigh cardinality
fiModelSeries has a high cardinality: 117 distinct valuesHigh cardinality
fiModelDescriptor has a high cardinality: 134 distinct valuesHigh cardinality
fiProductClassDesc has a high cardinality: 72 distinct valuesHigh cardinality
state has a high cardinality: 53 distinct valuesHigh cardinality
Ride_Control is highly imbalanced (52.8%)Imbalance
Transmission is highly imbalanced (75.3%)Imbalance
Engine_Horsepower is highly imbalanced (68.9%)Imbalance
Hydraulics_Flow is highly imbalanced (89.5%)Imbalance
Pattern_Changer is highly imbalanced (94.6%)Imbalance
Grouser_Type is highly imbalanced (60.9%)Imbalance
Differential_Type is highly imbalanced (92.0%)Imbalance
Steering_Controls is highly imbalanced (95.9%)Imbalance
auctioneerID has 17459 (4.7%) missing valuesMissing
MachineHoursCurrentMeter has 242638 (65.1%) missing valuesMissing
UsageBand has 304898 (81.8%) missing valuesMissing
fiSecondaryDesc has 122856 (32.9%) missing valuesMissing
fiModelSeries has 323889 (86.9%) missing valuesMissing
fiModelDescriptor has 302791 (81.2%) missing valuesMissing
ProductSize has 197516 (53.0%) missing valuesMissing
Drive_System has 273743 (73.4%) missing valuesMissing
Forks has 359124 (96.3%) missing valuesMissing
Pad_Type has 364445 (97.7%) missing valuesMissing
Ride_Control has 290119 (77.8%) missing valuesMissing
Stick has 296794 (79.6%) missing valuesMissing
Transmission has 221393 (59.4%) missing valuesMissing
Turbocharged has 369048 (99.0%) missing valuesMissing
Blade_Extension has 372379 (99.9%) missing valuesMissing
Blade_Width has 358112 (96.0%) missing valuesMissing
Enclosure_Type has 369554 (99.1%) missing valuesMissing
Engine_Horsepower has 349872 (93.8%) missing valuesMissing
Hydraulics has 77378 (20.7%) missing valuesMissing
Pushblock has 367338 (98.5%) missing valuesMissing
Ripper has 352845 (94.6%) missing valuesMissing
Scarifier has 361390 (96.9%) missing valuesMissing
Tip_Control has 364323 (97.7%) missing valuesMissing
Tire_Size has 327869 (87.9%) missing valuesMissing
Coupler has 344820 (92.5%) missing valuesMissing
Coupler_System has 369861 (99.2%) missing valuesMissing
Grouser_Tracks has 370062 (99.2%) missing valuesMissing
Hydraulics_Flow has 332206 (89.1%) missing valuesMissing
Track_Type has 283243 (76.0%) missing valuesMissing
Undercarriage_Pad_Width has 354851 (95.2%) missing valuesMissing
Stick_Length has 353869 (94.9%) missing valuesMissing
Thumb has 357241 (95.8%) missing valuesMissing
Pattern_Changer has 363802 (97.6%) missing valuesMissing
Grouser_Type has 283243 (76.0%) missing valuesMissing
Backhoe_Mounting has 372895 (> 99.9%) missing valuesMissing
Blade_Type has 308635 (82.8%) missing valuesMissing
Travel_Controls has 363493 (97.5%) missing valuesMissing
Differential_Type has 308097 (82.6%) missing valuesMissing
Steering_Controls has 308133 (82.6%) missing valuesMissing
SalesID has unique valuesUnique
MachineHoursCurrentMeter has 62260 (16.7%) zerosZeros
saleDayOfWeek has 22103 (5.9%) zerosZeros

Reproduction

Analysis started2023-06-25 23:52:59.731106
Analysis finished2023-06-25 23:54:31.467872
Duration1 minute and 31.74 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

SalesID
Real number (ℝ)

Distinct372915
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010255.6
Minimum1139246
Maximum6333349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 MiB
2023-06-25T19:54:31.614773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1139246
5-th percentile1217768.1
Q11415891.5
median1630519
Q32259277.5
95-th percentile4359822.3
Maximum6333349
Range5194103
Interquartile range (IQR)843386

Descriptive statistics

Standard deviation1094494.7
Coefficient of variation (CV)0.54445548
Kurtosis7.0493974
Mean2010255.6
Median Absolute Deviation (MAD)261434
Skewness2.6719756
Sum7.4965448 × 1011
Variance1.1979186 × 1012
MonotonicityNot monotonic
2023-06-25T19:54:31.750497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1646770 1
 
< 0.1%
1181577 1
 
< 0.1%
2404870 1
 
< 0.1%
1181578 1
 
< 0.1%
1181801 1
 
< 0.1%
1181839 1
 
< 0.1%
1181840 1
 
< 0.1%
2196192 1
 
< 0.1%
2438318 1
 
< 0.1%
2335112 1
 
< 0.1%
Other values (372905) 372905
> 99.9%
ValueCountFrequency (%)
1139246 1
< 0.1%
1139248 1
< 0.1%
1139249 1
< 0.1%
1139251 1
< 0.1%
1139253 1
< 0.1%
1139255 1
< 0.1%
1139256 1
< 0.1%
1139261 1
< 0.1%
1139272 1
< 0.1%
1139278 1
< 0.1%
ValueCountFrequency (%)
6333349 1
< 0.1%
6333348 1
< 0.1%
6333347 1
< 0.1%
6333345 1
< 0.1%
6333344 1
< 0.1%
6333343 1
< 0.1%
6333342 1
< 0.1%
6333341 1
< 0.1%
6333339 1
< 0.1%
6333338 1
< 0.1%

SalePrice
Real number (ℝ)

Distinct933
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32330.512
Minimum4750
Maximum142000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 MiB
2023-06-25T19:54:31.867939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4750
5-th percentile8500
Q115000
median25000
Q342000
95-th percentile83000
Maximum142000
Range137250
Interquartile range (IQR)27000

Descriptive statistics

Standard deviation23528.219
Coefficient of variation (CV)0.72774038
Kurtosis1.9123597
Mean32330.512
Median Absolute Deviation (MAD)12000
Skewness1.4547385
Sum1.2056533 × 1010
Variance5.5357708 × 108
MonotonicityNot monotonic
2023-06-25T19:54:31.978696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25000 7242
 
1.9%
20000 6913
 
1.9%
26000 6696
 
1.8%
15000 6573
 
1.8%
14000 6277
 
1.7%
16000 6275
 
1.7%
17000 6178
 
1.7%
18000 6118
 
1.6%
19000 6110
 
1.6%
24000 6110
 
1.6%
Other values (923) 308423
82.7%
ValueCountFrequency (%)
4750 153
 
< 0.1%
4800 16
 
< 0.1%
4850 4
 
< 0.1%
4900 17
 
< 0.1%
4935 1
 
< 0.1%
4950 2
 
< 0.1%
4987 2
 
< 0.1%
5000 703
0.2%
5100 35
 
< 0.1%
5150 1
 
< 0.1%
ValueCountFrequency (%)
142000 5
 
< 0.1%
141000 13
 
< 0.1%
140000 103
< 0.1%
139000 6
 
< 0.1%
138900 1
 
< 0.1%
138000 11
 
< 0.1%
137500 53
< 0.1%
137000 14
 
< 0.1%
136000 18
 
< 0.1%
135000 94
< 0.1%

MachineID
Real number (ℝ)

Distinct318066
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1222473.6
Minimum0
Maximum2486276
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size13.8 MiB
2023-06-25T19:54:32.107597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile201470.9
Q11084650.5
median1281261
Q31475833
95-th percentile1873247
Maximum2486276
Range2486276
Interquartile range (IQR)391182.5

Descriptive statistics

Standard deviation463304.2
Coefficient of variation (CV)0.37898912
Kurtosis0.78047048
Mean1222473.6
Median Absolute Deviation (MAD)195583
Skewness-0.59929103
Sum4.5587874 × 1011
Variance2.1465078 × 1011
MonotonicityNot monotonic
2023-06-25T19:54:32.226402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2283592 46
 
< 0.1%
2285830 44
 
< 0.1%
1896854 39
 
< 0.1%
1746392 34
 
< 0.1%
2268800 31
 
< 0.1%
1942724 29
 
< 0.1%
2282547 29
 
< 0.1%
2208545 27
 
< 0.1%
2300370 27
 
< 0.1%
2293171 26
 
< 0.1%
Other values (318056) 372583
99.9%
ValueCountFrequency (%)
0 2
< 0.1%
2 1
< 0.1%
13 1
< 0.1%
17 1
< 0.1%
52 1
< 0.1%
63 1
< 0.1%
66 1
< 0.1%
102 2
< 0.1%
113 1
< 0.1%
116 1
< 0.1%
ValueCountFrequency (%)
2486276 1
< 0.1%
2486275 1
< 0.1%
2486274 1
< 0.1%
2486273 1
< 0.1%
2486111 1
< 0.1%
2486110 1
< 0.1%
2485633 1
< 0.1%
2484864 1
< 0.1%
2484863 1
< 0.1%
2484862 1
< 0.1%

ModelID
Real number (ℝ)

Distinct4833
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6838.2878
Minimum28
Maximum37198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 MiB
2023-06-25T19:54:32.343614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile802.5
Q13263
median4604
Q38343
95-th percentile22074
Maximum37198
Range37170
Interquartile range (IQR)5080

Descriptive statistics

Standard deviation6202.3953
Coefficient of variation (CV)0.90700997
Kurtosis3.1854999
Mean6838.2878
Median Absolute Deviation (MAD)2409
Skewness1.7880698
Sum2.5501001 × 109
Variance38469707
MonotonicityNot monotonic
2023-06-25T19:54:32.458044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4605 5306
 
1.4%
3538 4845
 
1.3%
4604 4171
 
1.1%
3170 3950
 
1.1%
3362 3933
 
1.1%
3537 3642
 
1.0%
4603 3296
 
0.9%
3171 3248
 
0.9%
3357 3047
 
0.8%
3178 2954
 
0.8%
Other values (4823) 334523
89.7%
ValueCountFrequency (%)
28 38
 
< 0.1%
29 17
 
< 0.1%
31 12
 
< 0.1%
34 9
 
< 0.1%
43 683
0.2%
47 236
 
0.1%
50 10
 
< 0.1%
53 56
 
< 0.1%
55 3
 
< 0.1%
75 430
0.1%
ValueCountFrequency (%)
37198 2
 
< 0.1%
37197 16
< 0.1%
37196 14
< 0.1%
36933 2
 
< 0.1%
36928 1
 
< 0.1%
36914 1
 
< 0.1%
36894 1
 
< 0.1%
36885 1
 
< 0.1%
36883 1
 
< 0.1%
36880 1
 
< 0.1%

datasource
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.37663
Minimum121
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 MiB
2023-06-25T19:54:32.554851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile121
Q1132
median132
Q3136
95-th percentile149
Maximum173
Range52
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.8572695
Coefficient of variation (CV)0.072813672
Kurtosis6.517749
Mean135.37663
Median Absolute Deviation (MAD)0
Skewness2.4330397
Sum50483976
Variance97.165762
MonotonicityNot monotonic
2023-06-25T19:54:32.633921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
132 239231
64.2%
136 64778
 
17.4%
149 30856
 
8.3%
121 20519
 
5.5%
172 17530
 
4.7%
173 1
 
< 0.1%
ValueCountFrequency (%)
121 20519
 
5.5%
132 239231
64.2%
136 64778
 
17.4%
149 30856
 
8.3%
172 17530
 
4.7%
173 1
 
< 0.1%
ValueCountFrequency (%)
173 1
 
< 0.1%
172 17530
 
4.7%
149 30856
 
8.3%
136 64778
 
17.4%
132 239231
64.2%
121 20519
 
5.5%

auctioneerID
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing17459
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean6.4869013
Minimum0
Maximum99
Zeros431
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size13.8 MiB
2023-06-25T19:54:32.728951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q34
95-th percentile21
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation17.116211
Coefficient of variation (CV)2.6385804
Kurtosis23.071733
Mean6.4869013
Median Absolute Deviation (MAD)0
Skewness4.8327465
Sum2305808
Variance292.96468
MonotonicityNot monotonic
2023-06-25T19:54:32.825265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 180673
48.4%
2 50296
 
13.5%
3 25541
 
6.8%
4 18290
 
4.9%
6 11570
 
3.1%
99 10841
 
2.9%
7 7004
 
1.9%
8 6424
 
1.7%
5 6300
 
1.7%
10 4958
 
1.3%
Other values (20) 33559
 
9.0%
(Missing) 17459
 
4.7%
ValueCountFrequency (%)
0 431
 
0.1%
1 180673
48.4%
2 50296
 
13.5%
3 25541
 
6.8%
4 18290
 
4.9%
5 6300
 
1.7%
6 11570
 
3.1%
7 7004
 
1.9%
8 6424
 
1.7%
9 3268
 
0.9%
ValueCountFrequency (%)
99 10841
2.9%
28 755
 
0.2%
27 1052
 
0.3%
26 684
 
0.2%
25 861
 
0.2%
24 1337
 
0.4%
23 1162
 
0.3%
22 1042
 
0.3%
21 1280
 
0.3%
20 1965
 
0.5%

YearMade
Real number (ℝ)

Distinct70
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1993.9412
Minimum1937
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 MiB
2023-06-25T19:54:32.938180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1937
5-th percentile1975
Q11988
median1996
Q32001
95-th percentile2006
Maximum2014
Range77
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.579253
Coefficient of variation (CV)0.0048041802
Kurtosis0.20326391
Mean1993.9412
Median Absolute Deviation (MAD)6
Skewness-0.87515654
Sum7.435706 × 108
Variance91.762089
MonotonicityNot monotonic
2023-06-25T19:54:33.054200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 22048
 
5.9%
1998 21743
 
5.8%
2004 20874
 
5.6%
1999 19263
 
5.2%
1997 19259
 
5.2%
2000 17230
 
4.6%
1996 16993
 
4.6%
1995 15798
 
4.2%
2003 14649
 
3.9%
1994 14456
 
3.9%
Other values (60) 190602
51.1%
ValueCountFrequency (%)
1937 1
 
< 0.1%
1942 1
 
< 0.1%
1947 1
 
< 0.1%
1948 3
 
< 0.1%
1949 1
 
< 0.1%
1950 8
< 0.1%
1951 7
< 0.1%
1952 6
< 0.1%
1953 7
< 0.1%
1954 3
 
< 0.1%
ValueCountFrequency (%)
2014 2
 
< 0.1%
2013 1
 
< 0.1%
2012 1
 
< 0.1%
2011 31
 
< 0.1%
2010 58
 
< 0.1%
2009 212
 
0.1%
2008 1691
 
0.5%
2007 5044
 
1.4%
2006 13409
3.6%
2005 22048
5.9%

MachineHoursCurrentMeter
Real number (ℝ)

MISSING  ZEROS 

Distinct14316
Distinct (%)11.0%
Missing242638
Missing (%)65.1%
Infinite0
Infinite (%)0.0%
Mean2491.069
Minimum0
Maximum185100
Zeros62260
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size13.8 MiB
2023-06-25T19:54:33.178252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median380
Q33238
95-th percentile10139.2
Maximum185100
Range185100
Interquartile range (IQR)3238

Descriptive statistics

Standard deviation5745.5617
Coefficient of variation (CV)2.3064643
Kurtosis308.45129
Mean2491.069
Median Absolute Deviation (MAD)380
Skewness13.224764
Sum3.24529 × 108
Variance33011479
MonotonicityNot monotonic
2023-06-25T19:54:33.289517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 62260
 
16.7%
2000 114
 
< 0.1%
1000 110
 
< 0.1%
1500 96
 
< 0.1%
500 95
 
< 0.1%
24 90
 
< 0.1%
1200 85
 
< 0.1%
800 84
 
< 0.1%
1700 80
 
< 0.1%
1400 79
 
< 0.1%
Other values (14306) 67184
 
18.0%
(Missing) 242638
65.1%
ValueCountFrequency (%)
0 62260
16.7%
1 2
 
< 0.1%
2 18
 
< 0.1%
3 21
 
< 0.1%
4 35
 
< 0.1%
5 43
 
< 0.1%
6 19
 
< 0.1%
7 12
 
< 0.1%
8 16
 
< 0.1%
9 11
 
< 0.1%
ValueCountFrequency (%)
185100 1
< 0.1%
183500 1
< 0.1%
180300 1
< 0.1%
180000 1
< 0.1%
177800 1
< 0.1%
177200 1
< 0.1%
176700 1
< 0.1%
176300 1
< 0.1%
176100 1
< 0.1%
174900 1
< 0.1%

UsageBand
Categorical

Distinct3
Distinct (%)< 0.1%
Missing304898
Missing (%)81.8%
Memory size13.8 MiB
Medium
34384 
Low
21717 
High
11916 

Length

Max length6
Median length6
Mean length4.6917535
Min length3

Characters and Unicode

Total characters319119
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowLow
3rd rowLow
4th rowMedium
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium 34384
 
9.2%
Low 21717
 
5.8%
High 11916
 
3.2%
(Missing) 304898
81.8%

Length

2023-06-25T19:54:33.402272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:33.495679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
medium 34384
50.6%
low 21717
31.9%
high 11916
 
17.5%

Most occurring characters

ValueCountFrequency (%)
i 46300
14.5%
M 34384
10.8%
e 34384
10.8%
d 34384
10.8%
u 34384
10.8%
m 34384
10.8%
L 21717
6.8%
o 21717
6.8%
w 21717
6.8%
H 11916
 
3.7%
Other values (2) 23832
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 251102
78.7%
Uppercase Letter 68017
 
21.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 46300
18.4%
e 34384
13.7%
d 34384
13.7%
u 34384
13.7%
m 34384
13.7%
o 21717
8.6%
w 21717
8.6%
g 11916
 
4.7%
h 11916
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
M 34384
50.6%
L 21717
31.9%
H 11916
 
17.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 319119
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 46300
14.5%
M 34384
10.8%
e 34384
10.8%
d 34384
10.8%
u 34384
10.8%
m 34384
10.8%
L 21717
6.8%
o 21717
6.8%
w 21717
6.8%
H 11916
 
3.7%
Other values (2) 23832
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 319119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 46300
14.5%
M 34384
10.8%
e 34384
10.8%
d 34384
10.8%
u 34384
10.8%
m 34384
10.8%
L 21717
6.8%
o 21717
6.8%
w 21717
6.8%
H 11916
 
3.7%
Other values (2) 23832
7.5%

fiModelDesc
Categorical

Distinct4639
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size13.8 MiB
310G
 
5306
416C
 
4845
310E
 
4171
580K
 
3950
140G
 
3933
Other values (4634)
350710 

Length

Max length19
Median length17
Mean length4.667919
Min length1

Characters and Unicode

Total characters1740737
Distinct characters57
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique802 ?
Unique (%)0.2%

Sample

1st rowTD20
2nd rowA66
3rd rowD7G
4th rowA62
5th rowD3B

Common Values

ValueCountFrequency (%)
310G 5306
 
1.4%
416C 4845
 
1.3%
310E 4171
 
1.1%
580K 3950
 
1.1%
140G 3933
 
1.1%
416B 3657
 
1.0%
310D 3296
 
0.9%
580L 3248
 
0.9%
12G 3047
 
0.8%
580SUPER L 2954
 
0.8%
Other values (4629) 334508
89.7%

Length

2023-06-25T19:54:33.588654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
580super 5435
 
1.4%
310g 5306
 
1.4%
416c 4845
 
1.3%
310e 4171
 
1.1%
580k 3950
 
1.0%
140g 3933
 
1.0%
416b 3657
 
1.0%
l 3345
 
0.9%
310d 3296
 
0.9%
580l 3249
 
0.9%
Other values (4629) 338535
89.2%

Most occurring characters

ValueCountFrequency (%)
0 206863
 
11.9%
5 133270
 
7.7%
1 123334
 
7.1%
3 111962
 
6.4%
2 110918
 
6.4%
4 94278
 
5.4%
6 88555
 
5.1%
L 88355
 
5.1%
C 87507
 
5.0%
D 79400
 
4.6%
Other values (47) 616295
35.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1025981
58.9%
Uppercase Letter 687099
39.5%
Dash Punctuation 18504
 
1.1%
Space Separator 7148
 
0.4%
Other Punctuation 1909
 
0.1%
Lowercase Letter 63
 
< 0.1%
Math Symbol 25
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 88355
12.9%
C 87507
12.7%
D 79400
11.6%
G 54550
 
7.9%
I 46707
 
6.8%
E 44942
 
6.5%
B 42959
 
6.3%
P 39583
 
5.8%
S 27330
 
4.0%
H 26952
 
3.9%
Other values (16) 148814
21.7%
Lowercase Letter
ValueCountFrequency (%)
t 10
15.9%
e 8
12.7%
i 7
11.1%
o 6
9.5%
a 6
9.5%
g 4
 
6.3%
h 4
 
6.3%
m 4
 
6.3%
s 3
 
4.8%
r 3
 
4.8%
Other values (4) 8
12.7%
Decimal Number
ValueCountFrequency (%)
0 206863
20.2%
5 133270
13.0%
1 123334
12.0%
3 111962
10.9%
2 110918
10.8%
4 94278
9.2%
6 88555
8.6%
8 66204
 
6.5%
9 45917
 
4.5%
7 44680
 
4.4%
Other Punctuation
ValueCountFrequency (%)
. 1907
99.9%
/ 2
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 18504
100.0%
Space Separator
ValueCountFrequency (%)
7148
100.0%
Math Symbol
ValueCountFrequency (%)
+ 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1053575
60.5%
Latin 687162
39.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 88355
12.9%
C 87507
12.7%
D 79400
11.6%
G 54550
 
7.9%
I 46707
 
6.8%
E 44942
 
6.5%
B 42959
 
6.3%
P 39583
 
5.8%
S 27330
 
4.0%
H 26952
 
3.9%
Other values (30) 148877
21.7%
Common
ValueCountFrequency (%)
0 206863
19.6%
5 133270
12.6%
1 123334
11.7%
3 111962
10.6%
2 110918
10.5%
4 94278
8.9%
6 88555
8.4%
8 66204
 
6.3%
9 45917
 
4.4%
7 44680
 
4.2%
Other values (7) 27594
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1740737
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 206863
 
11.9%
5 133270
 
7.7%
1 123334
 
7.1%
3 111962
 
6.4%
2 110918
 
6.4%
4 94278
 
5.4%
6 88555
 
5.1%
L 88355
 
5.1%
C 87507
 
5.0%
D 79400
 
4.6%
Other values (47) 616295
35.4%

fiBaseModel
Categorical

Distinct1797
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size13.8 MiB
580
 
18623
310
 
17417
D6
 
12988
416
 
12585
D5
 
9398
Other values (1792)
301904 

Length

Max length13
Median length3
Mean length3.1776464
Min length1

Characters and Unicode

Total characters1184992
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique213 ?
Unique (%)0.1%

Sample

1st rowTD20
2nd rowA66
3rd rowD7
4th rowA62
5th rowD3

Common Values

ValueCountFrequency (%)
580 18623
 
5.0%
310 17417
 
4.7%
D6 12988
 
3.5%
416 12585
 
3.4%
D5 9398
 
2.5%
950 7282
 
2.0%
D3 6803
 
1.8%
D8 6505
 
1.7%
D4 6339
 
1.7%
12 5968
 
1.6%
Other values (1787) 269007
72.1%

Length

2023-06-25T19:54:33.691030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
580 18623
 
5.0%
310 17417
 
4.7%
d6 12988
 
3.5%
416 12585
 
3.4%
d5 9398
 
2.5%
950 7282
 
2.0%
d3 6803
 
1.8%
d8 6505
 
1.7%
d4 6339
 
1.7%
12 5968
 
1.6%
Other values (1760) 269408
72.2%

Most occurring characters

ValueCountFrequency (%)
0 206296
17.4%
5 128033
10.8%
1 116615
9.8%
3 106726
9.0%
2 104770
8.8%
4 94154
7.9%
6 83069
7.0%
8 65109
 
5.5%
D 60021
 
5.1%
9 45915
 
3.9%
Other values (29) 174284
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 992746
83.8%
Uppercase Letter 189875
 
16.0%
Other Punctuation 1876
 
0.2%
Space Separator 401
 
< 0.1%
Dash Punctuation 94
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 60021
31.6%
C 17712
 
9.3%
E 13357
 
7.0%
P 13135
 
6.9%
S 12809
 
6.7%
X 12321
 
6.5%
T 11992
 
6.3%
L 9054
 
4.8%
W 7831
 
4.1%
A 6672
 
3.5%
Other values (15) 24971
13.2%
Decimal Number
ValueCountFrequency (%)
0 206296
20.8%
5 128033
12.9%
1 116615
11.7%
3 106726
10.8%
2 104770
10.6%
4 94154
9.5%
6 83069
8.4%
8 65109
 
6.6%
9 45915
 
4.6%
7 42059
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 1875
99.9%
/ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
401
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 94
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 995117
84.0%
Latin 189875
 
16.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 60021
31.6%
C 17712
 
9.3%
E 13357
 
7.0%
P 13135
 
6.9%
S 12809
 
6.7%
X 12321
 
6.5%
T 11992
 
6.3%
L 9054
 
4.8%
W 7831
 
4.1%
A 6672
 
3.5%
Other values (15) 24971
13.2%
Common
ValueCountFrequency (%)
0 206296
20.7%
5 128033
12.9%
1 116615
11.7%
3 106726
10.7%
2 104770
10.5%
4 94154
9.5%
6 83069
8.3%
8 65109
 
6.5%
9 45915
 
4.6%
7 42059
 
4.2%
Other values (4) 2371
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1184992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 206296
17.4%
5 128033
10.8%
1 116615
9.8%
3 106726
9.0%
2 104770
8.8%
4 94154
7.9%
6 83069
7.0%
8 65109
 
5.5%
D 60021
 
5.1%
9 45915
 
3.9%
Other values (29) 174284
14.7%

fiSecondaryDesc
Categorical

HIGH CARDINALITY  MISSING 

Distinct163
Distinct (%)0.1%
Missing122856
Missing (%)32.9%
Memory size13.8 MiB
C
42005 
B
37260 
G
35920 
H
23606 
E
19345 
Other values (158)
91923 

Length

Max length13
Median length1
Mean length1.2390556
Min length1

Characters and Unicode

Total characters309837
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

1st rowG
2nd rowB
3rd rowC
4th rowH
5th rowF

Common Values

ValueCountFrequency (%)
C 42005
 
11.3%
B 37260
 
10.0%
G 35920
 
9.6%
H 23606
 
6.3%
E 19345
 
5.2%
D 19004
 
5.1%
F 8860
 
2.4%
K 7434
 
2.0%
M 5192
 
1.4%
L 5169
 
1.4%
Other values (153) 46264
 
12.4%
(Missing) 122856
32.9%

Length

2023-06-25T19:54:33.782060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c 42015
16.4%
b 37315
14.6%
g 35929
14.0%
h 23606
9.2%
e 19570
7.6%
d 19004
7.4%
f 8860
 
3.5%
l 8781
 
3.4%
k 8257
 
3.2%
m 6838
 
2.7%
Other values (139) 46223
18.0%

Most occurring characters

ValueCountFrequency (%)
C 46021
14.9%
B 37919
12.2%
G 37540
12.1%
E 30754
9.9%
H 23973
 
7.7%
D 19177
 
6.2%
L 16009
 
5.2%
S 11742
 
3.8%
R 11524
 
3.7%
P 10539
 
3.4%
Other values (27) 64639
20.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 302794
97.7%
Space Separator 6681
 
2.2%
Decimal Number 287
 
0.1%
Dash Punctuation 40
 
< 0.1%
Other Punctuation 35
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 46021
15.2%
B 37919
12.5%
G 37540
12.4%
E 30754
10.2%
H 23973
7.9%
D 19177
 
6.3%
L 16009
 
5.3%
S 11742
 
3.9%
R 11524
 
3.8%
P 10539
 
3.5%
Other values (14) 57596
19.0%
Decimal Number
ValueCountFrequency (%)
7 124
43.2%
3 113
39.4%
2 22
 
7.7%
5 14
 
4.9%
0 8
 
2.8%
1 4
 
1.4%
6 1
 
0.3%
9 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
# 14
40.0%
? 14
40.0%
. 7
20.0%
Space Separator
ValueCountFrequency (%)
6681
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 302794
97.7%
Common 7043
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 46021
15.2%
B 37919
12.5%
G 37540
12.4%
E 30754
10.2%
H 23973
7.9%
D 19177
 
6.3%
L 16009
 
5.3%
S 11742
 
3.9%
R 11524
 
3.8%
P 10539
 
3.5%
Other values (14) 57596
19.0%
Common
ValueCountFrequency (%)
6681
94.9%
7 124
 
1.8%
3 113
 
1.6%
- 40
 
0.6%
2 22
 
0.3%
# 14
 
0.2%
? 14
 
0.2%
5 14
 
0.2%
0 8
 
0.1%
. 7
 
0.1%
Other values (3) 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 309837
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 46021
14.9%
B 37919
12.2%
G 37540
12.1%
E 30754
9.9%
H 23973
 
7.7%
D 19177
 
6.2%
L 16009
 
5.2%
S 11742
 
3.8%
R 11524
 
3.7%
P 10539
 
3.4%
Other values (27) 64639
20.9%

fiModelSeries
Categorical

HIGH CARDINALITY  MISSING 

Distinct117
Distinct (%)0.2%
Missing323889
Missing (%)86.9%
Memory size13.8 MiB
II
13009 
LC
7731 
III
5099 
-1
3498 
-2
2779 
Other values (112)
16910 

Length

Max length11
Median length2
Mean length2.1981194
Min length1

Characters and Unicode

Total characters107765
Distinct characters46
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st rowII
2nd rowII
3rd rowII
4th rowII
5th row1

Common Values

ValueCountFrequency (%)
II 13009
 
3.5%
LC 7731
 
2.1%
III 5099
 
1.4%
-1 3498
 
0.9%
-2 2779
 
0.7%
-3 2148
 
0.6%
-6 1998
 
0.5%
-5 1841
 
0.5%
-12 1241
 
0.3%
7 698
 
0.2%
Other values (107) 8984
 
2.4%
(Missing) 323889
86.9%

Length

2023-06-25T19:54:33.871877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ii 13009
26.5%
lc 7731
15.8%
iii 5099
 
10.4%
1 3663
 
7.5%
2 3098
 
6.3%
6 2642
 
5.4%
3 2612
 
5.3%
5 2319
 
4.7%
7 1287
 
2.6%
12 1258
 
2.6%
Other values (91) 6308
12.9%

Most occurring characters

ValueCountFrequency (%)
I 41956
38.9%
- 18002
16.7%
L 8880
 
8.2%
C 8667
 
8.0%
1 6674
 
6.2%
2 5691
 
5.3%
3 3565
 
3.3%
5 3249
 
3.0%
6 2914
 
2.7%
7 1571
 
1.5%
Other values (36) 6596
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 63570
59.0%
Decimal Number 25240
 
23.4%
Dash Punctuation 18002
 
16.7%
Other Punctuation 813
 
0.8%
Math Symbol 105
 
0.1%
Lowercase Letter 35
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 41956
66.0%
L 8880
 
14.0%
C 8667
 
13.6%
V 1074
 
1.7%
M 684
 
1.1%
E 574
 
0.9%
N 522
 
0.8%
A 421
 
0.7%
T 241
 
0.4%
S 103
 
0.2%
Other values (13) 448
 
0.7%
Decimal Number
ValueCountFrequency (%)
1 6674
26.4%
2 5691
22.5%
3 3565
14.1%
5 3249
12.9%
6 2914
11.5%
7 1571
 
6.2%
8 800
 
3.2%
0 759
 
3.0%
4 16
 
0.1%
9 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
t 8
22.9%
e 6
17.1%
o 4
11.4%
m 4
11.4%
a 4
11.4%
i 3
 
8.6%
s 3
 
8.6%
r 3
 
8.6%
Other Punctuation
ValueCountFrequency (%)
# 354
43.5%
? 354
43.5%
. 105
 
12.9%
Dash Punctuation
ValueCountFrequency (%)
- 18002
100.0%
Math Symbol
ValueCountFrequency (%)
+ 105
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63605
59.0%
Common 44160
41.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 41956
66.0%
L 8880
 
14.0%
C 8667
 
13.6%
V 1074
 
1.7%
M 684
 
1.1%
E 574
 
0.9%
N 522
 
0.8%
A 421
 
0.7%
T 241
 
0.4%
S 103
 
0.2%
Other values (21) 483
 
0.8%
Common
ValueCountFrequency (%)
- 18002
40.8%
1 6674
 
15.1%
2 5691
 
12.9%
3 3565
 
8.1%
5 3249
 
7.4%
6 2914
 
6.6%
7 1571
 
3.6%
8 800
 
1.8%
0 759
 
1.7%
# 354
 
0.8%
Other values (5) 581
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107765
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 41956
38.9%
- 18002
16.7%
L 8880
 
8.2%
C 8667
 
8.0%
1 6674
 
6.2%
2 5691
 
5.3%
3 3565
 
3.3%
5 3249
 
3.0%
6 2914
 
2.7%
7 1571
 
1.5%
Other values (36) 6596
 
6.1%

fiModelDescriptor
Categorical

HIGH CARDINALITY  MISSING 

Distinct134
Distinct (%)0.2%
Missing302791
Missing (%)81.2%
Memory size13.8 MiB
L
15809 
LGP
15627 
LC
12310 
XL
6503 
6
2557 
Other values (129)
17318 

Length

Max length14
Median length10
Mean length1.9043551
Min length1

Characters and Unicode

Total characters133541
Distinct characters50
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

1st rowLC
2nd rowLGP
3rd rowLGP
4th rowLGP
5th rowLC

Common Values

ValueCountFrequency (%)
L 15809
 
4.2%
LGP 15627
 
4.2%
LC 12310
 
3.3%
XL 6503
 
1.7%
6 2557
 
0.7%
LT 2361
 
0.6%
5 1956
 
0.5%
CR 1772
 
0.5%
3 1454
 
0.4%
H 991
 
0.3%
Other values (124) 8784
 
2.4%
(Missing) 302791
81.2%

Length

2023-06-25T19:54:33.974518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
l 15809
22.5%
lgp 15636
22.3%
lc 12310
17.6%
xl 6503
9.3%
6 2557
 
3.6%
lt 2361
 
3.4%
5 1956
 
2.8%
cr 1772
 
2.5%
3 1454
 
2.1%
h 991
 
1.4%
Other values (122) 8780
12.5%

Most occurring characters

ValueCountFrequency (%)
L 54412
40.7%
P 15858
 
11.9%
G 15723
 
11.8%
C 15107
 
11.3%
X 7412
 
5.6%
T 4108
 
3.1%
R 3856
 
2.9%
S 2676
 
2.0%
6 2559
 
1.9%
5 1965
 
1.5%
Other values (40) 9865
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 125100
93.7%
Decimal Number 8223
 
6.2%
Other Punctuation 77
 
0.1%
Math Symbol 76
 
0.1%
Space Separator 29
 
< 0.1%
Lowercase Letter 28
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 54412
43.5%
P 15858
 
12.7%
G 15723
 
12.6%
C 15107
 
12.1%
X 7412
 
5.9%
T 4108
 
3.3%
R 3856
 
3.1%
S 2676
 
2.1%
H 1244
 
1.0%
Z 1015
 
0.8%
Other values (14) 3689
 
2.9%
Lowercase Letter
ValueCountFrequency (%)
h 4
14.3%
g 4
14.3%
i 4
14.3%
t 2
7.1%
f 2
7.1%
n 2
7.1%
c 2
7.1%
a 2
7.1%
e 2
7.1%
o 2
7.1%
Decimal Number
ValueCountFrequency (%)
6 2559
31.1%
5 1965
23.9%
3 1567
19.1%
7 926
 
11.3%
2 439
 
5.3%
0 305
 
3.7%
8 295
 
3.6%
4 116
 
1.4%
1 51
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 76
98.7%
/ 1
 
1.3%
Math Symbol
ValueCountFrequency (%)
+ 76
100.0%
Space Separator
ValueCountFrequency (%)
29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 125128
93.7%
Common 8413
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 54412
43.5%
P 15858
 
12.7%
G 15723
 
12.6%
C 15107
 
12.1%
X 7412
 
5.9%
T 4108
 
3.3%
R 3856
 
3.1%
S 2676
 
2.1%
H 1244
 
1.0%
Z 1015
 
0.8%
Other values (25) 3717
 
3.0%
Common
ValueCountFrequency (%)
6 2559
30.4%
5 1965
23.4%
3 1567
18.6%
7 926
 
11.0%
2 439
 
5.2%
0 305
 
3.6%
8 295
 
3.5%
4 116
 
1.4%
. 76
 
0.9%
+ 76
 
0.9%
Other values (5) 89
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 133541
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 54412
40.7%
P 15858
 
11.9%
G 15723
 
11.8%
C 15107
 
11.3%
X 7412
 
5.6%
T 4108
 
3.1%
R 3856
 
2.9%
S 2676
 
2.0%
6 2559
 
1.9%
5 1965
 
1.5%
Other values (40) 9865
 
7.4%

ProductSize
Categorical

Distinct6
Distinct (%)< 0.1%
Missing197516
Missing (%)53.0%
Memory size13.8 MiB
Medium
59334 
Large / Medium
46474 
Small
23743 
Mini
20876 
Large
19171 

Length

Max length14
Median length7
Mean length7.6700608
Min length4

Characters and Unicode

Total characters1345321
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowLarge
3rd rowMedium
4th rowCompact
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium 59334
 
15.9%
Large / Medium 46474
 
12.5%
Small 23743
 
6.4%
Mini 20876
 
5.6%
Large 19171
 
5.1%
Compact 5801
 
1.6%
(Missing) 197516
53.0%

Length

2023-06-25T19:54:34.073994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:34.185978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
medium 105808
39.4%
large 65645
24.5%
46474
17.3%
small 23743
 
8.8%
mini 20876
 
7.8%
compact 5801
 
2.2%

Most occurring characters

ValueCountFrequency (%)
e 171453
12.7%
i 147560
11.0%
m 135352
10.1%
M 126684
9.4%
d 105808
7.9%
u 105808
7.9%
a 95189
7.1%
92948
 
6.9%
r 65645
 
4.9%
g 65645
 
4.9%
Other values (10) 233229
17.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 984026
73.1%
Uppercase Letter 221873
 
16.5%
Space Separator 92948
 
6.9%
Other Punctuation 46474
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 171453
17.4%
i 147560
15.0%
m 135352
13.8%
d 105808
10.8%
u 105808
10.8%
a 95189
9.7%
r 65645
 
6.7%
g 65645
 
6.7%
l 47486
 
4.8%
n 20876
 
2.1%
Other values (4) 23204
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
M 126684
57.1%
L 65645
29.6%
S 23743
 
10.7%
C 5801
 
2.6%
Space Separator
ValueCountFrequency (%)
92948
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 46474
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1205899
89.6%
Common 139422
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 171453
14.2%
i 147560
12.2%
m 135352
11.2%
M 126684
10.5%
d 105808
8.8%
u 105808
8.8%
a 95189
7.9%
r 65645
 
5.4%
g 65645
 
5.4%
L 65645
 
5.4%
Other values (8) 121110
10.0%
Common
ValueCountFrequency (%)
92948
66.7%
/ 46474
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1345321
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 171453
12.7%
i 147560
11.0%
m 135352
10.1%
M 126684
9.4%
d 105808
7.9%
u 105808
7.9%
a 95189
7.1%
92948
 
6.9%
r 65645
 
4.9%
g 65645
 
4.9%
Other values (10) 233229
17.3%

fiProductClassDesc
Categorical

Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.8 MiB
Backhoe Loader - 14.0 to 15.0 Ft Standard Digging Depth
54547 
Track Type Tractor, Dozer - 20.0 to 75.0 Horsepower
 
15082
Wheel Loader - 150.0 to 175.0 Horsepower
 
14587
Track Type Tractor, Dozer - 85.0 to 105.0 Horsepower
 
14412
Hydraulic Excavator, Track - 21.0 to 24.0 Metric Tons
 
12573
Other values (67)
261714 

Length

Max length64
Median length57
Mean length49.799989
Min length27

Characters and Unicode

Total characters18571163
Distinct characters54
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrack Type Tractor, Dozer - 105.0 to 130.0 Horsepower
2nd rowWheel Loader - 120.0 to 135.0 Horsepower
3rd rowTrack Type Tractor, Dozer - 190.0 to 260.0 Horsepower
4th rowWheel Loader - Unidentified
5th rowTrack Type Tractor, Dozer - 20.0 to 75.0 Horsepower

Common Values

ValueCountFrequency (%)
Backhoe Loader - 14.0 to 15.0 Ft Standard Digging Depth 54547
 
14.6%
Track Type Tractor, Dozer - 20.0 to 75.0 Horsepower 15082
 
4.0%
Wheel Loader - 150.0 to 175.0 Horsepower 14587
 
3.9%
Track Type Tractor, Dozer - 85.0 to 105.0 Horsepower 14412
 
3.9%
Hydraulic Excavator, Track - 21.0 to 24.0 Metric Tons 12573
 
3.4%
Track Type Tractor, Dozer - 130.0 to 160.0 Horsepower 10989
 
2.9%
Track Type Tractor, Dozer - 260.0 + Horsepower 10354
 
2.8%
Backhoe Loader - 15.0 to 16.0 Ft Standard Digging Depth 10170
 
2.7%
Hydraulic Excavator, Track - 12.0 to 14.0 Metric Tons 10092
 
2.7%
Wheel Loader - 120.0 to 135.0 Horsepower 10007
 
2.7%
Other values (62) 210102
56.3%

Length

2023-06-25T19:54:34.297382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
389300
 
12.1%
to 349814
 
10.8%
loader 183319
 
5.7%
track 166329
 
5.2%
horsepower 164122
 
5.1%
excavator 91214
 
2.8%
hydraulic 91214
 
2.8%
tons 91093
 
2.8%
metric 91093
 
2.8%
backhoe 76398
 
2.4%
Other values (73) 1533381
47.5%

Most occurring characters

ValueCountFrequency (%)
2854362
15.4%
r 1401019
 
7.5%
o 1316927
 
7.1%
e 1188214
 
6.4%
a 1065360
 
5.7%
0 1021476
 
5.5%
t 976191
 
5.3%
. 718608
 
3.9%
c 631522
 
3.4%
1 534902
 
2.9%
Other values (44) 6862582
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10174606
54.8%
Space Separator 2854362
 
15.4%
Decimal Number 2495754
 
13.4%
Uppercase Letter 1769555
 
9.5%
Other Punctuation 884937
 
4.8%
Dash Punctuation 372915
 
2.0%
Math Symbol 18980
 
0.1%
Open Punctuation 27
 
< 0.1%
Close Punctuation 27
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1401019
13.8%
o 1316927
12.9%
e 1188214
11.7%
a 1065360
10.5%
t 976191
9.6%
c 631522
 
6.2%
d 493844
 
4.9%
i 462709
 
4.5%
p 392948
 
3.9%
n 286442
 
2.8%
Other values (14) 1959430
19.3%
Uppercase Letter
ValueCountFrequency (%)
T 407652
23.0%
H 255336
14.4%
L 223424
12.6%
D 222063
12.5%
S 155182
 
8.8%
M 114360
 
6.5%
E 91214
 
5.2%
B 76398
 
4.3%
F 73474
 
4.2%
W 66067
 
3.7%
Other values (3) 84385
 
4.8%
Decimal Number
ValueCountFrequency (%)
0 1021476
40.9%
1 534902
21.4%
5 273558
 
11.0%
2 176686
 
7.1%
4 133428
 
5.3%
3 102332
 
4.1%
6 93801
 
3.8%
7 85136
 
3.4%
8 45440
 
1.8%
9 28995
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 718608
81.2%
, 166329
 
18.8%
Space Separator
ValueCountFrequency (%)
2854362
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 372915
100.0%
Math Symbol
ValueCountFrequency (%)
+ 18980
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11944161
64.3%
Common 6627002
35.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1401019
 
11.7%
o 1316927
 
11.0%
e 1188214
 
9.9%
a 1065360
 
8.9%
t 976191
 
8.2%
c 631522
 
5.3%
d 493844
 
4.1%
i 462709
 
3.9%
T 407652
 
3.4%
p 392948
 
3.3%
Other values (27) 3607775
30.2%
Common
ValueCountFrequency (%)
2854362
43.1%
0 1021476
 
15.4%
. 718608
 
10.8%
1 534902
 
8.1%
- 372915
 
5.6%
5 273558
 
4.1%
2 176686
 
2.7%
, 166329
 
2.5%
4 133428
 
2.0%
3 102332
 
1.5%
Other values (7) 272406
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18571163
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2854362
15.4%
r 1401019
 
7.5%
o 1316927
 
7.1%
e 1188214
 
6.4%
a 1065360
 
5.7%
0 1021476
 
5.5%
t 976191
 
5.3%
. 718608
 
3.9%
c 631522
 
3.4%
1 534902
 
2.9%
Other values (44) 6862582
37.0%

state
Categorical

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.8 MiB
Florida
60971 
Texas
49135 
California
27534 
Washington
 
14537
Georgia
 
13799
Other values (48)
206939 

Length

Max length14
Median length12
Mean length8.0555033
Min length4

Characters and Unicode

Total characters3004018
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTexas
2nd rowFlorida
3rd rowFlorida
4th rowFlorida
5th rowFlorida

Common Values

ValueCountFrequency (%)
Florida 60971
16.3%
Texas 49135
 
13.2%
California 27534
 
7.4%
Washington 14537
 
3.9%
Georgia 13799
 
3.7%
Maryland 12190
 
3.3%
Mississippi 11760
 
3.2%
Ohio 10871
 
2.9%
Colorado 10802
 
2.9%
Illinois 10607
 
2.8%
Other values (43) 150709
40.4%

Length

2023-06-25T19:54:34.404140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
florida 60971
 
14.7%
texas 49135
 
11.8%
california 27534
 
6.6%
new 22951
 
5.5%
carolina 17989
 
4.3%
washington 14539
 
3.5%
georgia 13799
 
3.3%
maryland 12190
 
2.9%
mississippi 11760
 
2.8%
ohio 10871
 
2.6%
Other values (46) 173571
41.8%

Most occurring characters

ValueCountFrequency (%)
a 391970
13.0%
i 334912
 
11.1%
o 265236
 
8.8%
n 224158
 
7.5%
s 205032
 
6.8%
e 198066
 
6.6%
r 198033
 
6.6%
l 170829
 
5.7%
d 98820
 
3.3%
t 65643
 
2.2%
Other values (36) 851319
28.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2546311
84.8%
Uppercase Letter 415312
 
13.8%
Space Separator 42395
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 391970
15.4%
i 334912
13.2%
o 265236
10.4%
n 224158
8.8%
s 205032
8.1%
e 198066
7.8%
r 198033
7.8%
l 170829
6.7%
d 98820
 
3.9%
t 65643
 
2.6%
Other values (14) 393612
15.5%
Uppercase Letter
ValueCountFrequency (%)
C 63660
15.3%
F 60971
14.7%
T 58525
14.1%
M 47946
11.5%
N 40511
9.8%
A 21769
 
5.2%
W 19097
 
4.6%
I 17264
 
4.2%
O 13812
 
3.3%
G 13799
 
3.3%
Other values (11) 57958
14.0%
Space Separator
ValueCountFrequency (%)
42395
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2961623
98.6%
Common 42395
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 391970
13.2%
i 334912
11.3%
o 265236
 
9.0%
n 224158
 
7.6%
s 205032
 
6.9%
e 198066
 
6.7%
r 198033
 
6.7%
l 170829
 
5.8%
d 98820
 
3.3%
t 65643
 
2.2%
Other values (35) 808924
27.3%
Common
ValueCountFrequency (%)
42395
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3004018
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 391970
13.0%
i 334912
 
11.1%
o 265236
 
8.8%
n 224158
 
7.5%
s 205032
 
6.8%
e 198066
 
6.6%
r 198033
 
6.6%
l 170829
 
5.7%
d 98820
 
3.3%
t 65643
 
2.2%
Other values (36) 851319
28.3%

ProductGroup
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.8 MiB
TEX
91214 
BL
76398 
TTT
75115 
WL
66067 
SSL
40854 

Length

Max length3
Median length3
Mean length2.555577
Min length2

Characters and Unicode

Total characters953013
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTTT
2nd rowWL
3rd rowTTT
4th rowWL
5th rowTTT

Common Values

ValueCountFrequency (%)
TEX 91214
24.5%
BL 76398
20.5%
TTT 75115
20.1%
WL 66067
17.7%
SSL 40854
11.0%
MG 23267
 
6.2%

Length

2023-06-25T19:54:34.499580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:34.597999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
tex 91214
24.5%
bl 76398
20.5%
ttt 75115
20.1%
wl 66067
17.7%
ssl 40854
11.0%
mg 23267
 
6.2%

Most occurring characters

ValueCountFrequency (%)
T 316559
33.2%
L 183319
19.2%
E 91214
 
9.6%
X 91214
 
9.6%
S 81708
 
8.6%
B 76398
 
8.0%
W 66067
 
6.9%
M 23267
 
2.4%
G 23267
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 953013
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 316559
33.2%
L 183319
19.2%
E 91214
 
9.6%
X 91214
 
9.6%
S 81708
 
8.6%
B 76398
 
8.0%
W 66067
 
6.9%
M 23267
 
2.4%
G 23267
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 953013
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 316559
33.2%
L 183319
19.2%
E 91214
 
9.6%
X 91214
 
9.6%
S 81708
 
8.6%
B 76398
 
8.0%
W 66067
 
6.9%
M 23267
 
2.4%
G 23267
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 953013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 316559
33.2%
L 183319
19.2%
E 91214
 
9.6%
X 91214
 
9.6%
S 81708
 
8.6%
B 76398
 
8.0%
W 66067
 
6.9%
M 23267
 
2.4%
G 23267
 
2.4%

ProductGroupDesc
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.8 MiB
Track Excavators
91214 
Backhoe Loaders
76398 
Track Type Tractors
75115 
Wheel Loader
66067 
Skid Steer Loaders
40854 

Length

Max length19
Median length16
Mean length15.722687
Min length12

Characters and Unicode

Total characters5863226
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrack Type Tractors
2nd rowWheel Loader
3rd rowTrack Type Tractors
4th rowWheel Loader
5th rowTrack Type Tractors

Common Values

ValueCountFrequency (%)
Track Excavators 91214
24.5%
Backhoe Loaders 76398
20.5%
Track Type Tractors 75115
20.1%
Wheel Loader 66067
17.7%
Skid Steer Loaders 40854
11.0%
Motor Graders 23267
 
6.2%

Length

2023-06-25T19:54:34.716471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:34.824149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
track 166329
19.3%
loaders 117252
13.6%
excavators 91214
10.6%
backhoe 76398
8.9%
type 75115
8.7%
tractors 75115
8.7%
wheel 66067
 
7.7%
loader 66067
 
7.7%
skid 40854
 
4.7%
steer 40854
 
4.7%
Other values (2) 46534
 
5.4%

Most occurring characters

ValueCountFrequency (%)
a 706856
12.1%
r 701747
12.0%
e 571941
9.8%
488884
 
8.3%
o 472580
 
8.1%
c 409056
 
7.0%
T 316559
 
5.4%
s 306848
 
5.2%
k 283581
 
4.8%
d 247440
 
4.2%
Other values (15) 1357734
23.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4512543
77.0%
Uppercase Letter 861799
 
14.7%
Space Separator 488884
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 706856
15.7%
r 701747
15.6%
e 571941
12.7%
o 472580
10.5%
c 409056
9.1%
s 306848
6.8%
k 283581
6.3%
d 247440
 
5.5%
t 230450
 
5.1%
h 142465
 
3.2%
Other values (6) 439579
9.7%
Uppercase Letter
ValueCountFrequency (%)
T 316559
36.7%
L 183319
21.3%
E 91214
 
10.6%
S 81708
 
9.5%
B 76398
 
8.9%
W 66067
 
7.7%
M 23267
 
2.7%
G 23267
 
2.7%
Space Separator
ValueCountFrequency (%)
488884
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5374342
91.7%
Common 488884
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 706856
13.2%
r 701747
13.1%
e 571941
10.6%
o 472580
8.8%
c 409056
 
7.6%
T 316559
 
5.9%
s 306848
 
5.7%
k 283581
 
5.3%
d 247440
 
4.6%
t 230450
 
4.3%
Other values (14) 1127284
21.0%
Common
ValueCountFrequency (%)
488884
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5863226
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 706856
12.1%
r 701747
12.0%
e 571941
9.8%
488884
 
8.3%
o 472580
 
8.1%
c 409056
 
7.0%
T 316559
 
5.4%
s 306848
 
5.2%
k 283581
 
4.8%
d 247440
 
4.2%
Other values (15) 1357734
23.2%

Drive_System
Categorical

Distinct4
Distinct (%)< 0.1%
Missing273743
Missing (%)73.4%
Memory size13.8 MiB
Two Wheel Drive
44120 
Four Wheel Drive
32002 
No
22341 
All Wheel Drive
 
709

Length

Max length16
Median length15
Mean length12.394113
Min length2

Characters and Unicode

Total characters1229149
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowFour Wheel Drive
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
Two Wheel Drive 44120
 
11.8%
Four Wheel Drive 32002
 
8.6%
No 22341
 
6.0%
All Wheel Drive 709
 
0.2%
(Missing) 273743
73.4%

Length

2023-06-25T19:54:34.927698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:35.020188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
wheel 76831
30.4%
drive 76831
30.4%
two 44120
17.5%
four 32002
12.7%
no 22341
 
8.8%
all 709
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 230493
18.8%
153662
12.5%
r 108833
8.9%
o 98463
8.0%
l 78249
 
6.4%
W 76831
 
6.3%
h 76831
 
6.3%
D 76831
 
6.3%
i 76831
 
6.3%
v 76831
 
6.3%
Other values (6) 175294
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 822653
66.9%
Uppercase Letter 252834
 
20.6%
Space Separator 153662
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 230493
28.0%
r 108833
13.2%
o 98463
12.0%
l 78249
 
9.5%
h 76831
 
9.3%
i 76831
 
9.3%
v 76831
 
9.3%
w 44120
 
5.4%
u 32002
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
W 76831
30.4%
D 76831
30.4%
T 44120
17.5%
F 32002
12.7%
N 22341
 
8.8%
A 709
 
0.3%
Space Separator
ValueCountFrequency (%)
153662
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1075487
87.5%
Common 153662
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 230493
21.4%
r 108833
10.1%
o 98463
9.2%
l 78249
 
7.3%
W 76831
 
7.1%
h 76831
 
7.1%
D 76831
 
7.1%
i 76831
 
7.1%
v 76831
 
7.1%
T 44120
 
4.1%
Other values (5) 131174
12.2%
Common
ValueCountFrequency (%)
153662
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1229149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 230493
18.8%
153662
12.5%
r 108833
8.9%
o 98463
8.0%
l 78249
 
6.4%
W 76831
 
6.3%
h 76831
 
6.3%
D 76831
 
6.3%
i 76831
 
6.3%
v 76831
 
6.3%
Other values (6) 175294
14.3%

Enclosure
Categorical

Distinct5
Distinct (%)< 0.1%
Missing264
Missing (%)0.1%
Memory size13.8 MiB
OROPS
162358 
EROPS
123878 
EROPS w AC
86397 
EROPS AC
 
16
NO ROPS
 
2

Length

Max length10
Median length5
Mean length6.1593609
Min length5

Characters and Unicode

Total characters2295292
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOROPS
2nd rowOROPS
3rd rowOROPS
4th rowEROPS
5th rowOROPS

Common Values

ValueCountFrequency (%)
OROPS 162358
43.5%
EROPS 123878
33.2%
EROPS w AC 86397
23.2%
EROPS AC 16
 
< 0.1%
NO ROPS 2
 
< 0.1%
(Missing) 264
 
0.1%

Length

2023-06-25T19:54:35.103905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:35.204750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
erops 210291
38.6%
orops 162358
29.8%
ac 86413
15.8%
w 86397
15.8%
no 2
 
< 0.1%
rops 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 535011
23.3%
R 372651
16.2%
P 372651
16.2%
S 372651
16.2%
E 210291
 
9.2%
172812
 
7.5%
A 86413
 
3.8%
C 86413
 
3.8%
w 86397
 
3.8%
N 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2036083
88.7%
Space Separator 172812
 
7.5%
Lowercase Letter 86397
 
3.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 535011
26.3%
R 372651
18.3%
P 372651
18.3%
S 372651
18.3%
E 210291
 
10.3%
A 86413
 
4.2%
C 86413
 
4.2%
N 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
172812
100.0%
Lowercase Letter
ValueCountFrequency (%)
w 86397
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2122480
92.5%
Common 172812
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 535011
25.2%
R 372651
17.6%
P 372651
17.6%
S 372651
17.6%
E 210291
 
9.9%
A 86413
 
4.1%
C 86413
 
4.1%
w 86397
 
4.1%
N 2
 
< 0.1%
Common
ValueCountFrequency (%)
172812
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2295292
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 535011
23.3%
R 372651
16.2%
P 372651
16.2%
S 372651
16.2%
E 210291
 
9.2%
172812
 
7.5%
A 86413
 
3.8%
C 86413
 
3.8%
w 86397
 
3.8%
N 2
 
< 0.1%

Forks
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing359124
Missing (%)96.3%
Memory size11.6 MiB
True
 
13791
(Missing)
359124 
ValueCountFrequency (%)
True 13791
 
3.7%
(Missing) 359124
96.3%
2023-06-25T19:54:35.292863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pad_Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing364445
Missing (%)97.7%
Memory size13.8 MiB
Reversible
5800 
Street
2645 
Grouser
 
25

Length

Max length10
Median length10
Mean length8.7420307
Min length6

Characters and Unicode

Total characters74045
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStreet
2nd rowStreet
3rd rowStreet
4th rowStreet
5th rowStreet

Common Values

ValueCountFrequency (%)
Reversible 5800
 
1.6%
Street 2645
 
0.7%
Grouser 25
 
< 0.1%
(Missing) 364445
97.7%

Length

2023-06-25T19:54:35.366163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:35.464649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
reversible 5800
68.5%
street 2645
31.2%
grouser 25
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 22715
30.7%
r 8495
 
11.5%
s 5825
 
7.9%
R 5800
 
7.8%
v 5800
 
7.8%
i 5800
 
7.8%
b 5800
 
7.8%
l 5800
 
7.8%
t 5290
 
7.1%
S 2645
 
3.6%
Other values (3) 75
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65575
88.6%
Uppercase Letter 8470
 
11.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 22715
34.6%
r 8495
 
13.0%
s 5825
 
8.9%
v 5800
 
8.8%
i 5800
 
8.8%
b 5800
 
8.8%
l 5800
 
8.8%
t 5290
 
8.1%
o 25
 
< 0.1%
u 25
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
R 5800
68.5%
S 2645
31.2%
G 25
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 74045
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 22715
30.7%
r 8495
 
11.5%
s 5825
 
7.9%
R 5800
 
7.8%
v 5800
 
7.8%
i 5800
 
7.8%
b 5800
 
7.8%
l 5800
 
7.8%
t 5290
 
7.1%
S 2645
 
3.6%
Other values (3) 75
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74045
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 22715
30.7%
r 8495
 
11.5%
s 5825
 
7.9%
R 5800
 
7.8%
v 5800
 
7.8%
i 5800
 
7.8%
b 5800
 
7.8%
l 5800
 
7.8%
t 5290
 
7.1%
S 2645
 
3.6%
Other values (3) 75
 
0.1%

Ride_Control
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing290119
Missing (%)77.8%
Memory size11.6 MiB
False
74438 
True
 
8358
(Missing)
290119 
ValueCountFrequency (%)
False 74438
 
20.0%
True 8358
 
2.2%
(Missing) 290119
77.8%
2023-06-25T19:54:35.569759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Stick
Categorical

Distinct2
Distinct (%)< 0.1%
Missing296794
Missing (%)79.6%
Memory size13.8 MiB
Standard
46491 
Extended
29630 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters608968
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard
2nd rowStandard
3rd rowStandard
4th rowStandard
5th rowStandard

Common Values

ValueCountFrequency (%)
Standard 46491
 
12.5%
Extended 29630
 
7.9%
(Missing) 296794
79.6%

Length

2023-06-25T19:54:35.675748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:35.782970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
standard 46491
61.1%
extended 29630
38.9%

Most occurring characters

ValueCountFrequency (%)
d 152242
25.0%
a 92982
15.3%
t 76121
12.5%
n 76121
12.5%
e 59260
 
9.7%
S 46491
 
7.6%
r 46491
 
7.6%
E 29630
 
4.9%
x 29630
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 532847
87.5%
Uppercase Letter 76121
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 152242
28.6%
a 92982
17.5%
t 76121
14.3%
n 76121
14.3%
e 59260
 
11.1%
r 46491
 
8.7%
x 29630
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
S 46491
61.1%
E 29630
38.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 608968
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 152242
25.0%
a 92982
15.3%
t 76121
12.5%
n 76121
12.5%
e 59260
 
9.7%
S 46491
 
7.6%
r 46491
 
7.6%
E 29630
 
4.9%
x 29630
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 608968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 152242
25.0%
a 92982
15.3%
t 76121
12.5%
n 76121
12.5%
e 59260
 
9.7%
S 46491
 
7.6%
r 46491
 
7.6%
E 29630
 
4.9%
x 29630
 
4.9%

Transmission
Categorical

IMBALANCE  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing221393
Missing (%)59.4%
Memory size13.8 MiB
Standard
134104 
Powershift
 
9867
Powershuttle
 
3947
Hydrostatic
 
3167
Direct Drive
 
284
Other values (2)
 
153

Length

Max length12
Median length8
Mean length8.3056454
Min length8

Characters and Unicode

Total characters1258488
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect Drive
2nd rowStandard
3rd rowStandard
4th rowStandard
5th rowPowershift

Common Values

ValueCountFrequency (%)
Standard 134104
36.0%
Powershift 9867
 
2.6%
Powershuttle 3947
 
1.1%
Hydrostatic 3167
 
0.8%
Direct Drive 284
 
0.1%
Autoshift 114
 
< 0.1%
AutoShift 39
 
< 0.1%
(Missing) 221393
59.4%

Length

2023-06-25T19:54:35.871712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:35.979835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
standard 134104
88.3%
powershift 9867
 
6.5%
powershuttle 3947
 
2.6%
hydrostatic 3167
 
2.1%
direct 284
 
0.2%
drive 284
 
0.2%
autoshift 153
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 271375
21.6%
d 271375
21.6%
t 158789
12.6%
r 151653
12.1%
S 134143
10.7%
n 134104
10.7%
e 18329
 
1.5%
o 17134
 
1.4%
s 17095
 
1.4%
h 13967
 
1.1%
Other values (13) 70524
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1106359
87.9%
Uppercase Letter 151845
 
12.1%
Space Separator 284
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 271375
24.5%
d 271375
24.5%
t 158789
14.4%
r 151653
13.7%
n 134104
12.1%
e 18329
 
1.7%
o 17134
 
1.5%
s 17095
 
1.5%
h 13967
 
1.3%
w 13814
 
1.2%
Other values (7) 38724
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
S 134143
88.3%
P 13814
 
9.1%
H 3167
 
2.1%
D 568
 
0.4%
A 153
 
0.1%
Space Separator
ValueCountFrequency (%)
284
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1258204
> 99.9%
Common 284
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 271375
21.6%
d 271375
21.6%
t 158789
12.6%
r 151653
12.1%
S 134143
10.7%
n 134104
10.7%
e 18329
 
1.5%
o 17134
 
1.4%
s 17095
 
1.4%
h 13967
 
1.1%
Other values (12) 70240
 
5.6%
Common
ValueCountFrequency (%)
284
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1258488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 271375
21.6%
d 271375
21.6%
t 158789
12.6%
r 151653
12.1%
S 134143
10.7%
n 134104
10.7%
e 18329
 
1.5%
o 17134
 
1.4%
s 17095
 
1.4%
h 13967
 
1.1%
Other values (13) 70524
 
5.6%

Turbocharged
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing369048
Missing (%)99.0%
Memory size11.6 MiB
True
 
3867
(Missing)
369048 
ValueCountFrequency (%)
True 3867
 
1.0%
(Missing) 369048
99.0%
2023-06-25T19:54:36.079062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Blade_Extension
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing372379
Missing (%)99.9%
Memory size11.6 MiB
True
 
536
(Missing)
372379 
ValueCountFrequency (%)
True 536
 
0.1%
(Missing) 372379
99.9%
2023-06-25T19:54:36.163264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Blade_Width
Categorical

Distinct5
Distinct (%)< 0.1%
Missing358112
Missing (%)96.0%
Memory size13.8 MiB
14'
9042 
12'
4532 
16'
 
885
13'
 
281
<12'
 
63

Length

Max length4
Median length3
Mean length3.0042559
Min length3

Characters and Unicode

Total characters44472
Distinct characters7
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12'
2nd row14'
3rd row12'
4th row12'
5th row16'

Common Values

ValueCountFrequency (%)
14' 9042
 
2.4%
12' 4532
 
1.2%
16' 885
 
0.2%
13' 281
 
0.1%
<12' 63
 
< 0.1%
(Missing) 358112
96.0%

Length

2023-06-25T19:54:36.247949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:36.345639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
14 9042
61.1%
12 4595
31.0%
16 885
 
6.0%
13 281
 
1.9%

Most occurring characters

ValueCountFrequency (%)
1 14803
33.3%
' 14803
33.3%
4 9042
20.3%
2 4595
 
10.3%
6 885
 
2.0%
3 281
 
0.6%
< 63
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29606
66.6%
Other Punctuation 14803
33.3%
Math Symbol 63
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14803
50.0%
4 9042
30.5%
2 4595
 
15.5%
6 885
 
3.0%
3 281
 
0.9%
Other Punctuation
ValueCountFrequency (%)
' 14803
100.0%
Math Symbol
ValueCountFrequency (%)
< 63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14803
33.3%
' 14803
33.3%
4 9042
20.3%
2 4595
 
10.3%
6 885
 
2.0%
3 281
 
0.6%
< 63
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14803
33.3%
' 14803
33.3%
4 9042
20.3%
2 4595
 
10.3%
6 885
 
2.0%
3 281
 
0.6%
< 63
 
0.1%

Enclosure_Type
Categorical

Distinct2
Distinct (%)0.1%
Missing369554
Missing (%)99.1%
Memory size13.8 MiB
Low Profile
2567 
High Profile
794 

Length

Max length12
Median length11
Mean length11.236239
Min length11

Characters and Unicode

Total characters37765
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow Profile
2nd rowHigh Profile
3rd rowLow Profile
4th rowLow Profile
5th rowHigh Profile

Common Values

ValueCountFrequency (%)
Low Profile 2567
 
0.7%
High Profile 794
 
0.2%
(Missing) 369554
99.1%

Length

2023-06-25T19:54:36.433992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:36.530224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
profile 3361
50.0%
low 2567
38.2%
high 794
 
11.8%

Most occurring characters

ValueCountFrequency (%)
o 5928
15.7%
i 4155
11.0%
3361
8.9%
P 3361
8.9%
r 3361
8.9%
f 3361
8.9%
l 3361
8.9%
e 3361
8.9%
L 2567
6.8%
w 2567
6.8%
Other values (3) 2382
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27682
73.3%
Uppercase Letter 6722
 
17.8%
Space Separator 3361
 
8.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 5928
21.4%
i 4155
15.0%
r 3361
12.1%
f 3361
12.1%
l 3361
12.1%
e 3361
12.1%
w 2567
9.3%
g 794
 
2.9%
h 794
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
P 3361
50.0%
L 2567
38.2%
H 794
 
11.8%
Space Separator
ValueCountFrequency (%)
3361
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34404
91.1%
Common 3361
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 5928
17.2%
i 4155
12.1%
P 3361
9.8%
r 3361
9.8%
f 3361
9.8%
l 3361
9.8%
e 3361
9.8%
L 2567
7.5%
w 2567
7.5%
H 794
 
2.3%
Other values (2) 1588
 
4.6%
Common
ValueCountFrequency (%)
3361
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37765
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 5928
15.7%
i 4155
11.0%
3361
8.9%
P 3361
8.9%
r 3361
8.9%
f 3361
8.9%
l 3361
8.9%
e 3361
8.9%
L 2567
6.8%
w 2567
6.8%
Other values (3) 2382
6.3%

Engine_Horsepower
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing349872
Missing (%)93.8%
Memory size13.8 MiB
No
21754 
Variable
 
1289

Length

Max length8
Median length2
Mean length2.3356334
Min length2

Characters and Unicode

Total characters53820
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 21754
 
5.8%
Variable 1289
 
0.3%
(Missing) 349872
93.8%

Length

2023-06-25T19:54:36.614547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:36.698798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 21754
94.4%
variable 1289
 
5.6%

Most occurring characters

ValueCountFrequency (%)
N 21754
40.4%
o 21754
40.4%
a 2578
 
4.8%
V 1289
 
2.4%
r 1289
 
2.4%
i 1289
 
2.4%
b 1289
 
2.4%
l 1289
 
2.4%
e 1289
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 30777
57.2%
Uppercase Letter 23043
42.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 21754
70.7%
a 2578
 
8.4%
r 1289
 
4.2%
i 1289
 
4.2%
b 1289
 
4.2%
l 1289
 
4.2%
e 1289
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
N 21754
94.4%
V 1289
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 53820
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 21754
40.4%
o 21754
40.4%
a 2578
 
4.8%
V 1289
 
2.4%
r 1289
 
2.4%
i 1289
 
2.4%
b 1289
 
2.4%
l 1289
 
2.4%
e 1289
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 21754
40.4%
o 21754
40.4%
a 2578
 
4.8%
V 1289
 
2.4%
r 1289
 
2.4%
i 1289
 
2.4%
b 1289
 
2.4%
l 1289
 
2.4%
e 1289
 
2.4%

Hydraulics
Categorical

Distinct11
Distinct (%)< 0.1%
Missing77378
Missing (%)20.7%
Memory size13.8 MiB
2 Valve
131417 
Standard
92283 
Auxiliary
40136 
Base + 1 Function
22563 
3 Valve
 
5486
Other values (6)
 
3652

Length

Max length17
Median length9
Mean length8.3690164
Min length7

Characters and Unicode

Total characters2473354
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2 Valve
2nd row2 Valve
3rd row2 Valve
4th row2 Valve
5th row2 Valve

Common Values

ValueCountFrequency (%)
2 Valve 131417
35.2%
Standard 92283
24.7%
Auxiliary 40136
 
10.8%
Base + 1 Function 22563
 
6.1%
3 Valve 5486
 
1.5%
4 Valve 3011
 
0.8%
Base + 3 Function 298
 
0.1%
Base + 2 Function 127
 
< 0.1%
Base + 5 Function 90
 
< 0.1%
Base + 4 Function 77
 
< 0.1%
(Missing) 77378
20.7%

Length

2023-06-25T19:54:36.770576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
valve 139914
27.7%
2 131544
26.0%
standard 92283
18.3%
auxiliary 40136
 
7.9%
base 23204
 
4.6%
23204
 
4.6%
function 23204
 
4.6%
1 22563
 
4.5%
3 5784
 
1.1%
4 3088
 
0.6%
Other values (2) 139
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 387820
15.7%
209526
 
8.5%
d 184566
 
7.5%
l 180050
 
7.3%
e 163118
 
6.6%
V 139914
 
5.7%
v 139914
 
5.7%
n 138691
 
5.6%
r 132419
 
5.4%
2 131544
 
5.3%
Other values (18) 665792
26.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1758765
71.1%
Uppercase Letter 318741
 
12.9%
Space Separator 209526
 
8.5%
Decimal Number 163118
 
6.6%
Math Symbol 23204
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 387820
22.1%
d 184566
10.5%
l 180050
10.2%
e 163118
9.3%
v 139914
 
8.0%
n 138691
 
7.9%
r 132419
 
7.5%
t 115487
 
6.6%
i 103476
 
5.9%
u 63340
 
3.6%
Other values (5) 149884
 
8.5%
Decimal Number
ValueCountFrequency (%)
2 131544
80.6%
1 22563
 
13.8%
3 5784
 
3.5%
4 3088
 
1.9%
5 90
 
0.1%
6 49
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
V 139914
43.9%
S 92283
29.0%
A 40136
 
12.6%
B 23204
 
7.3%
F 23204
 
7.3%
Space Separator
ValueCountFrequency (%)
209526
100.0%
Math Symbol
ValueCountFrequency (%)
+ 23204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2077506
84.0%
Common 395848
 
16.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 387820
18.7%
d 184566
8.9%
l 180050
8.7%
e 163118
7.9%
V 139914
 
6.7%
v 139914
 
6.7%
n 138691
 
6.7%
r 132419
 
6.4%
t 115487
 
5.6%
i 103476
 
5.0%
Other values (10) 392051
18.9%
Common
ValueCountFrequency (%)
209526
52.9%
2 131544
33.2%
+ 23204
 
5.9%
1 22563
 
5.7%
3 5784
 
1.5%
4 3088
 
0.8%
5 90
 
< 0.1%
6 49
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2473354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 387820
15.7%
209526
 
8.5%
d 184566
 
7.5%
l 180050
 
7.3%
e 163118
 
6.6%
V 139914
 
5.7%
v 139914
 
5.7%
n 138691
 
5.6%
r 132419
 
5.4%
2 131544
 
5.3%
Other values (18) 665792
26.9%

Pushblock
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing367338
Missing (%)98.5%
Memory size11.6 MiB
True
 
5577
(Missing)
367338 
ValueCountFrequency (%)
True 5577
 
1.5%
(Missing) 367338
98.5%
2023-06-25T19:54:37.250299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Ripper
Categorical

Distinct3
Distinct (%)< 0.1%
Missing352845
Missing (%)94.6%
Memory size13.8 MiB
Multi Shank
7659 
Yes
7572 
Single Shank
4839 

Length

Max length12
Median length11
Mean length8.22287
Min length3

Characters and Unicode

Total characters165033
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowSingle Shank
3rd rowMulti Shank
4th rowMulti Shank
5th rowYes

Common Values

ValueCountFrequency (%)
Multi Shank 7659
 
2.1%
Yes 7572
 
2.0%
Single Shank 4839
 
1.3%
(Missing) 352845
94.6%

Length

2023-06-25T19:54:37.336650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:37.430392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
shank 12498
38.4%
multi 7659
23.5%
yes 7572
23.2%
single 4839
 
14.9%

Most occurring characters

ValueCountFrequency (%)
S 17337
10.5%
n 17337
10.5%
l 12498
 
7.6%
i 12498
 
7.6%
12498
 
7.6%
h 12498
 
7.6%
a 12498
 
7.6%
k 12498
 
7.6%
e 12411
 
7.5%
M 7659
 
4.6%
Other values (5) 35301
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 119967
72.7%
Uppercase Letter 32568
 
19.7%
Space Separator 12498
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 17337
14.5%
l 12498
10.4%
i 12498
10.4%
h 12498
10.4%
a 12498
10.4%
k 12498
10.4%
e 12411
10.3%
u 7659
6.4%
t 7659
6.4%
s 7572
6.3%
Uppercase Letter
ValueCountFrequency (%)
S 17337
53.2%
M 7659
23.5%
Y 7572
23.2%
Space Separator
ValueCountFrequency (%)
12498
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 152535
92.4%
Common 12498
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 17337
11.4%
n 17337
11.4%
l 12498
8.2%
i 12498
8.2%
h 12498
8.2%
a 12498
8.2%
k 12498
8.2%
e 12411
8.1%
M 7659
 
5.0%
u 7659
 
5.0%
Other values (4) 27642
18.1%
Common
ValueCountFrequency (%)
12498
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 17337
10.5%
n 17337
10.5%
l 12498
 
7.6%
i 12498
 
7.6%
12498
 
7.6%
h 12498
 
7.6%
a 12498
 
7.6%
k 12498
 
7.6%
e 12411
 
7.5%
M 7659
 
4.6%
Other values (5) 35301
21.4%

Scarifier
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing361390
Missing (%)96.9%
Memory size11.6 MiB
True
 
11525
(Missing)
361390 
ValueCountFrequency (%)
True 11525
 
3.1%
(Missing) 361390
96.9%
2023-06-25T19:54:37.520596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Tip_Control
Categorical

Distinct2
Distinct (%)< 0.1%
Missing364323
Missing (%)97.7%
Memory size13.8 MiB
Sideshift & Tip
6760 
Tip
1832 

Length

Max length15
Median length15
Mean length12.441341
Min length3

Characters and Unicode

Total characters106896
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSideshift & Tip
2nd rowSideshift & Tip
3rd rowSideshift & Tip
4th rowSideshift & Tip
5th rowTip

Common Values

ValueCountFrequency (%)
Sideshift & Tip 6760
 
1.8%
Tip 1832
 
0.5%
(Missing) 364323
97.7%

Length

2023-06-25T19:54:37.607414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:37.725055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
tip 8592
38.9%
sideshift 6760
30.6%
6760
30.6%

Most occurring characters

ValueCountFrequency (%)
i 22112
20.7%
13520
12.6%
T 8592
 
8.0%
p 8592
 
8.0%
S 6760
 
6.3%
d 6760
 
6.3%
e 6760
 
6.3%
s 6760
 
6.3%
h 6760
 
6.3%
f 6760
 
6.3%
Other values (2) 13520
12.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 71264
66.7%
Uppercase Letter 15352
 
14.4%
Space Separator 13520
 
12.6%
Other Punctuation 6760
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 22112
31.0%
p 8592
 
12.1%
d 6760
 
9.5%
e 6760
 
9.5%
s 6760
 
9.5%
h 6760
 
9.5%
f 6760
 
9.5%
t 6760
 
9.5%
Uppercase Letter
ValueCountFrequency (%)
T 8592
56.0%
S 6760
44.0%
Space Separator
ValueCountFrequency (%)
13520
100.0%
Other Punctuation
ValueCountFrequency (%)
& 6760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 86616
81.0%
Common 20280
 
19.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 22112
25.5%
T 8592
 
9.9%
p 8592
 
9.9%
S 6760
 
7.8%
d 6760
 
7.8%
e 6760
 
7.8%
s 6760
 
7.8%
h 6760
 
7.8%
f 6760
 
7.8%
t 6760
 
7.8%
Common
ValueCountFrequency (%)
13520
66.7%
& 6760
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106896
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 22112
20.7%
13520
12.6%
T 8592
 
8.0%
p 8592
 
8.0%
S 6760
 
6.3%
d 6760
 
6.3%
e 6760
 
6.3%
s 6760
 
6.3%
h 6760
 
6.3%
f 6760
 
6.3%
Other values (2) 13520
12.6%

Tire_Size
Categorical

Distinct16
Distinct (%)< 0.1%
Missing327869
Missing (%)87.9%
Memory size13.8 MiB
20.5
14518 
14"
8165 
23.5
8052 
26.5
4091 
17.5
3551 
Other values (11)
6669 

Length

Max length7
Median length4
Mean length3.8714647
Min length3

Characters and Unicode

Total characters174394
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20.5
2nd row20.5
3rd row14"
4th row14"
5th row14"

Common Values

ValueCountFrequency (%)
20.5 14518
 
3.9%
14" 8165
 
2.2%
23.5 8052
 
2.2%
26.5 4091
 
1.1%
17.5 3551
 
1.0%
29.5 2390
 
0.6%
17.5" 1665
 
0.4%
20.5" 668
 
0.2%
13" 655
 
0.2%
15.5 541
 
0.1%
Other values (6) 750
 
0.2%
(Missing) 327869
87.9%

Length

2023-06-25T19:54:37.823709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20.5 15186
33.7%
23.5 8335
18.5%
14 8165
18.1%
17.5 5216
 
11.6%
26.5 4091
 
9.1%
29.5 2390
 
5.3%
15.5 938
 
2.1%
13 655
 
1.5%
7.0 43
 
0.1%
23.1 16
 
< 0.1%
Other values (2) 14
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
5 37094
21.3%
. 36215
20.8%
2 30018
17.2%
0 15240
8.7%
1 15001
8.6%
" 11900
 
6.8%
3 9006
 
5.2%
4 8165
 
4.7%
7 5259
 
3.0%
6 4091
 
2.3%
Other values (6) 2405
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 126264
72.4%
Other Punctuation 48115
 
27.6%
Lowercase Letter 12
 
< 0.1%
Space Separator 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 37094
29.4%
2 30018
23.8%
0 15240
12.1%
1 15001
11.9%
3 9006
 
7.1%
4 8165
 
6.5%
7 5259
 
4.2%
6 4091
 
3.2%
9 2390
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
i 3
25.0%
n 3
25.0%
c 3
25.0%
h 3
25.0%
Other Punctuation
ValueCountFrequency (%)
. 36215
75.3%
" 11900
 
24.7%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 174382
> 99.9%
Latin 12
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 37094
21.3%
. 36215
20.8%
2 30018
17.2%
0 15240
8.7%
1 15001
8.6%
" 11900
 
6.8%
3 9006
 
5.2%
4 8165
 
4.7%
7 5259
 
3.0%
6 4091
 
2.3%
Other values (2) 2393
 
1.4%
Latin
ValueCountFrequency (%)
i 3
25.0%
n 3
25.0%
c 3
25.0%
h 3
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174394
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 37094
21.3%
. 36215
20.8%
2 30018
17.2%
0 15240
8.7%
1 15001
8.6%
" 11900
 
6.8%
3 9006
 
5.2%
4 8165
 
4.7%
7 5259
 
3.0%
6 4091
 
2.3%
Other values (6) 2405
 
1.4%

Coupler
Categorical

Distinct2
Distinct (%)< 0.1%
Missing344820
Missing (%)92.5%
Memory size13.8 MiB
Manual
22297 
Hydraulic
5798 

Length

Max length9
Median length6
Mean length6.6191137
Min length6

Characters and Unicode

Total characters185964
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Manual 22297
 
6.0%
Hydraulic 5798
 
1.6%
(Missing) 344820
92.5%

Length

2023-06-25T19:54:37.943251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:38.060957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
manual 22297
79.4%
hydraulic 5798
 
20.6%

Most occurring characters

ValueCountFrequency (%)
a 50392
27.1%
u 28095
15.1%
l 28095
15.1%
M 22297
12.0%
n 22297
12.0%
H 5798
 
3.1%
y 5798
 
3.1%
d 5798
 
3.1%
r 5798
 
3.1%
i 5798
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 157869
84.9%
Uppercase Letter 28095
 
15.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 50392
31.9%
u 28095
17.8%
l 28095
17.8%
n 22297
14.1%
y 5798
 
3.7%
d 5798
 
3.7%
r 5798
 
3.7%
i 5798
 
3.7%
c 5798
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
M 22297
79.4%
H 5798
 
20.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 185964
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 50392
27.1%
u 28095
15.1%
l 28095
15.1%
M 22297
12.0%
n 22297
12.0%
H 5798
 
3.1%
y 5798
 
3.1%
d 5798
 
3.1%
r 5798
 
3.1%
i 5798
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 185964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 50392
27.1%
u 28095
15.1%
l 28095
15.1%
M 22297
12.0%
n 22297
12.0%
H 5798
 
3.1%
y 5798
 
3.1%
d 5798
 
3.1%
r 5798
 
3.1%
i 5798
 
3.1%

Coupler_System
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing369861
Missing (%)99.2%
Memory size11.6 MiB
True
 
3054
(Missing)
369861 
ValueCountFrequency (%)
True 3054
 
0.8%
(Missing) 369861
99.2%
2023-06-25T19:54:38.143694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Grouser_Tracks
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing370062
Missing (%)99.2%
Memory size11.6 MiB
True
 
2853
(Missing)
370062 
ValueCountFrequency (%)
True 2853
 
0.8%
(Missing) 370062
99.2%
2023-06-25T19:54:38.209907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Hydraulics_Flow
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing332206
Missing (%)89.1%
Memory size13.8 MiB
Standard
40147 
High Flow
 
562

Length

Max length9
Median length8
Mean length8.0138053
Min length8

Characters and Unicode

Total characters326234
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard
2nd rowStandard
3rd rowStandard
4th rowStandard
5th rowStandard

Common Values

ValueCountFrequency (%)
Standard 40147
 
10.8%
High Flow 562
 
0.2%
(Missing) 332206
89.1%

Length

2023-06-25T19:54:38.278447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:38.366875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
standard 40147
97.3%
high 562
 
1.4%
flow 562
 
1.4%

Most occurring characters

ValueCountFrequency (%)
a 80294
24.6%
d 80294
24.6%
S 40147
12.3%
t 40147
12.3%
n 40147
12.3%
r 40147
12.3%
H 562
 
0.2%
i 562
 
0.2%
g 562
 
0.2%
h 562
 
0.2%
Other values (5) 2810
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 284401
87.2%
Uppercase Letter 41271
 
12.7%
Space Separator 562
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 80294
28.2%
d 80294
28.2%
t 40147
14.1%
n 40147
14.1%
r 40147
14.1%
i 562
 
0.2%
g 562
 
0.2%
h 562
 
0.2%
l 562
 
0.2%
o 562
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
S 40147
97.3%
H 562
 
1.4%
F 562
 
1.4%
Space Separator
ValueCountFrequency (%)
562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 325672
99.8%
Common 562
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 80294
24.7%
d 80294
24.7%
S 40147
12.3%
t 40147
12.3%
n 40147
12.3%
r 40147
12.3%
H 562
 
0.2%
i 562
 
0.2%
g 562
 
0.2%
h 562
 
0.2%
Other values (4) 2248
 
0.7%
Common
ValueCountFrequency (%)
562
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 326234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 80294
24.6%
d 80294
24.6%
S 40147
12.3%
t 40147
12.3%
n 40147
12.3%
r 40147
12.3%
H 562
 
0.2%
i 562
 
0.2%
g 562
 
0.2%
h 562
 
0.2%
Other values (5) 2810
 
0.9%

Track_Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing283243
Missing (%)76.0%
Memory size13.8 MiB
Steel
77300 
Rubber
12372 

Length

Max length6
Median length5
Mean length5.1379695
Min length5

Characters and Unicode

Total characters460732
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSteel
2nd rowSteel
3rd rowSteel
4th rowSteel
5th rowSteel

Common Values

ValueCountFrequency (%)
Steel 77300
 
20.7%
Rubber 12372
 
3.3%
(Missing) 283243
76.0%

Length

2023-06-25T19:54:38.441202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:38.536563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
steel 77300
86.2%
rubber 12372
 
13.8%

Most occurring characters

ValueCountFrequency (%)
e 166972
36.2%
S 77300
16.8%
t 77300
16.8%
l 77300
16.8%
b 24744
 
5.4%
R 12372
 
2.7%
u 12372
 
2.7%
r 12372
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 371060
80.5%
Uppercase Letter 89672
 
19.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 166972
45.0%
t 77300
20.8%
l 77300
20.8%
b 24744
 
6.7%
u 12372
 
3.3%
r 12372
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
S 77300
86.2%
R 12372
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 460732
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 166972
36.2%
S 77300
16.8%
t 77300
16.8%
l 77300
16.8%
b 24744
 
5.4%
R 12372
 
2.7%
u 12372
 
2.7%
r 12372
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 460732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 166972
36.2%
S 77300
16.8%
t 77300
16.8%
l 77300
16.8%
b 24744
 
5.4%
R 12372
 
2.7%
u 12372
 
2.7%
r 12372
 
2.7%

Undercarriage_Pad_Width
Categorical

Distinct18
Distinct (%)0.1%
Missing354851
Missing (%)95.2%
Memory size13.8 MiB
32 inch
4731 
28 inch
2845 
24 inch
2612 
20 inch
2266 
30 inch
1455 
Other values (13)
4155 

Length

Max length9
Median length7
Mean length7.0002214
Min length7

Characters and Unicode

Total characters126452
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24 inch
2nd row30 inch
3rd row30 inch
4th row28 inch
5th row24 inch

Common Values

ValueCountFrequency (%)
32 inch 4731
 
1.3%
28 inch 2845
 
0.8%
24 inch 2612
 
0.7%
20 inch 2266
 
0.6%
30 inch 1455
 
0.4%
36 inch 1373
 
0.4%
18 inch 1225
 
0.3%
34 inch 497
 
0.1%
16 inch 402
 
0.1%
31 inch 169
 
< 0.1%
Other values (8) 489
 
0.1%
(Missing) 354851
95.2%

Length

2023-06-25T19:54:38.651093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inch 18064
50.0%
32 4731
 
13.1%
28 2845
 
7.9%
24 2612
 
7.2%
20 2266
 
6.3%
30 1455
 
4.0%
36 1373
 
3.8%
18 1225
 
3.4%
34 497
 
1.4%
16 402
 
1.1%
Other values (9) 658
 
1.8%

Most occurring characters

ValueCountFrequency (%)
18064
14.3%
i 18064
14.3%
n 18064
14.3%
c 18064
14.3%
h 18064
14.3%
2 12909
10.2%
3 8389
6.6%
8 4070
 
3.2%
0 3721
 
2.9%
4 3156
 
2.5%
Other values (5) 3887
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 72256
57.1%
Decimal Number 36130
28.6%
Space Separator 18064
 
14.3%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 12909
35.7%
3 8389
23.2%
8 4070
 
11.3%
0 3721
 
10.3%
4 3156
 
8.7%
1 1869
 
5.2%
6 1859
 
5.1%
7 119
 
0.3%
5 38
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
i 18064
25.0%
n 18064
25.0%
c 18064
25.0%
h 18064
25.0%
Space Separator
ValueCountFrequency (%)
18064
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 72256
57.1%
Common 54196
42.9%

Most frequent character per script

Common
ValueCountFrequency (%)
18064
33.3%
2 12909
23.8%
3 8389
15.5%
8 4070
 
7.5%
0 3721
 
6.9%
4 3156
 
5.8%
1 1869
 
3.4%
6 1859
 
3.4%
7 119
 
0.2%
5 38
 
0.1%
Latin
ValueCountFrequency (%)
i 18064
25.0%
n 18064
25.0%
c 18064
25.0%
h 18064
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18064
14.3%
i 18064
14.3%
n 18064
14.3%
c 18064
14.3%
h 18064
14.3%
2 12909
10.2%
3 8389
6.6%
8 4070
 
3.2%
0 3721
 
2.9%
4 3156
 
2.5%
Other values (5) 3887
 
3.1%

Stick_Length
Categorical

Distinct27
Distinct (%)0.1%
Missing353869
Missing (%)94.9%
Memory size13.8 MiB
9' 6"
5378 
10' 6"
3250 
11' 0"
1383 
9' 10"
1380 
9' 8"
1367 
Other values (22)
6288 

Length

Max length7
Median length5
Mean length5.5804368
Min length5

Characters and Unicode

Total characters106285
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row10' 6"
2nd row8' 6"
3rd row8' 6"
4th row8' 6"
5th row9' 6"

Common Values

ValueCountFrequency (%)
9' 6" 5378
 
1.4%
10' 6" 3250
 
0.9%
11' 0" 1383
 
0.4%
9' 10" 1380
 
0.4%
9' 8" 1367
 
0.4%
9' 7" 1319
 
0.4%
12' 10" 1050
 
0.3%
10' 2" 937
 
0.3%
8' 6" 792
 
0.2%
8' 2" 535
 
0.1%
Other values (17) 1655
 
0.4%
(Missing) 353869
94.9%

Length

2023-06-25T19:54:38.750548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9 9617
25.2%
6 9464
24.8%
10 7813
20.5%
8 3312
 
8.7%
11 1674
 
4.4%
2 1473
 
3.9%
12 1449
 
3.8%
0 1383
 
3.6%
7 1333
 
3.5%
4 318
 
0.8%
Other values (7) 256
 
0.7%

Most occurring characters

ValueCountFrequency (%)
' 19046
17.9%
19046
17.9%
" 19046
17.9%
1 12736
12.0%
9 9622
9.1%
6 9464
8.9%
0 9196
8.7%
8 3312
 
3.1%
2 2922
 
2.7%
7 1333
 
1.3%
Other values (3) 562
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49147
46.2%
Other Punctuation 38092
35.8%
Space Separator 19046
 
17.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12736
25.9%
9 9622
19.6%
6 9464
19.3%
0 9196
18.7%
8 3312
 
6.7%
2 2922
 
5.9%
7 1333
 
2.7%
4 325
 
0.7%
5 168
 
0.3%
3 69
 
0.1%
Other Punctuation
ValueCountFrequency (%)
' 19046
50.0%
" 19046
50.0%
Space Separator
ValueCountFrequency (%)
19046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 106285
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
' 19046
17.9%
19046
17.9%
" 19046
17.9%
1 12736
12.0%
9 9622
9.1%
6 9464
8.9%
0 9196
8.7%
8 3312
 
3.1%
2 2922
 
2.7%
7 1333
 
1.3%
Other values (3) 562
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106285
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 19046
17.9%
19046
17.9%
" 19046
17.9%
1 12736
12.0%
9 9622
9.1%
6 9464
8.9%
0 9196
8.7%
8 3312
 
3.1%
2 2922
 
2.7%
7 1333
 
1.3%
Other values (3) 562
 
0.5%

Thumb
Categorical

Distinct2
Distinct (%)< 0.1%
Missing357241
Missing (%)95.8%
Memory size13.8 MiB
Manual
8654 
Hydraulic
7020 

Length

Max length9
Median length6
Mean length7.3436264
Min length6

Characters and Unicode

Total characters115104
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHydraulic
2nd rowHydraulic
3rd rowManual
4th rowManual
5th rowHydraulic

Common Values

ValueCountFrequency (%)
Manual 8654
 
2.3%
Hydraulic 7020
 
1.9%
(Missing) 357241
95.8%

Length

2023-06-25T19:54:38.844666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:38.932987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
manual 8654
55.2%
hydraulic 7020
44.8%

Most occurring characters

ValueCountFrequency (%)
a 24328
21.1%
u 15674
13.6%
l 15674
13.6%
M 8654
 
7.5%
n 8654
 
7.5%
H 7020
 
6.1%
y 7020
 
6.1%
d 7020
 
6.1%
r 7020
 
6.1%
i 7020
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 99430
86.4%
Uppercase Letter 15674
 
13.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 24328
24.5%
u 15674
15.8%
l 15674
15.8%
n 8654
 
8.7%
y 7020
 
7.1%
d 7020
 
7.1%
r 7020
 
7.1%
i 7020
 
7.1%
c 7020
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
M 8654
55.2%
H 7020
44.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 115104
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 24328
21.1%
u 15674
13.6%
l 15674
13.6%
M 8654
 
7.5%
n 8654
 
7.5%
H 7020
 
6.1%
y 7020
 
6.1%
d 7020
 
6.1%
r 7020
 
6.1%
i 7020
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 24328
21.1%
u 15674
13.6%
l 15674
13.6%
M 8654
 
7.5%
n 8654
 
7.5%
H 7020
 
6.1%
y 7020
 
6.1%
d 7020
 
6.1%
r 7020
 
6.1%
i 7020
 
6.1%

Pattern_Changer
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing363802
Missing (%)97.6%
Memory size11.6 MiB
True
 
9057
False
 
56
(Missing)
363802 
ValueCountFrequency (%)
True 9057
 
2.4%
False 56
 
< 0.1%
(Missing) 363802
97.6%
2023-06-25T19:54:39.007925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Grouser_Type
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing283243
Missing (%)76.0%
Memory size13.8 MiB
Double
75866 
Triple
13804 
Single
 
2

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters538032
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDouble
2nd rowTriple
3rd rowDouble
4th rowDouble
5th rowDouble

Common Values

ValueCountFrequency (%)
Double 75866
 
20.3%
Triple 13804
 
3.7%
Single 2
 
< 0.1%
(Missing) 283243
76.0%

Length

2023-06-25T19:54:39.076342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:39.176372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
double 75866
84.6%
triple 13804
 
15.4%
single 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l 89672
16.7%
e 89672
16.7%
D 75866
14.1%
o 75866
14.1%
u 75866
14.1%
b 75866
14.1%
i 13806
 
2.6%
T 13804
 
2.6%
r 13804
 
2.6%
p 13804
 
2.6%
Other values (3) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 448360
83.3%
Uppercase Letter 89672
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 89672
20.0%
e 89672
20.0%
o 75866
16.9%
u 75866
16.9%
b 75866
16.9%
i 13806
 
3.1%
r 13804
 
3.1%
p 13804
 
3.1%
n 2
 
< 0.1%
g 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
D 75866
84.6%
T 13804
 
15.4%
S 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 538032
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 89672
16.7%
e 89672
16.7%
D 75866
14.1%
o 75866
14.1%
u 75866
14.1%
b 75866
14.1%
i 13806
 
2.6%
T 13804
 
2.6%
r 13804
 
2.6%
p 13804
 
2.6%
Other values (3) 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 538032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 89672
16.7%
e 89672
16.7%
D 75866
14.1%
o 75866
14.1%
u 75866
14.1%
b 75866
14.1%
i 13806
 
2.6%
T 13804
 
2.6%
r 13804
 
2.6%
p 13804
 
2.6%
Other values (3) 6
 
< 0.1%

Backhoe_Mounting
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)5.0%
Missing372895
Missing (%)> 99.9%
Memory size11.6 MiB
True
 
20
(Missing)
372895 
ValueCountFrequency (%)
True 20
 
< 0.1%
(Missing) 372895
> 99.9%
2023-06-25T19:54:39.292673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Blade_Type
Categorical

Distinct9
Distinct (%)< 0.1%
Missing308635
Missing (%)82.8%
Memory size13.8 MiB
PAT
35909 
Straight
12500 
Semi U
8306 
VPAT
3620 
U
 
1803
Other values (4)
 
2142

Length

Max length8
Median length3
Mean length4.4021157
Min length1

Characters and Unicode

Total characters282968
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStraight
2nd rowStraight
3rd rowPAT
4th rowStraight
5th rowPAT

Common Values

ValueCountFrequency (%)
PAT 35909
 
9.6%
Straight 12500
 
3.4%
Semi U 8306
 
2.2%
VPAT 3620
 
1.0%
U 1803
 
0.5%
Angle 1560
 
0.4%
No 549
 
0.1%
Landfill 23
 
< 0.1%
Coal 10
 
< 0.1%
(Missing) 308635
82.8%

Length

2023-06-25T19:54:39.375615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:39.485134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
pat 35909
49.5%
straight 12500
 
17.2%
u 10109
 
13.9%
semi 8306
 
11.4%
vpat 3620
 
5.0%
angle 1560
 
2.1%
no 549
 
0.8%
landfill 23
 
< 0.1%
coal 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 41089
14.5%
P 39529
14.0%
T 39529
14.0%
t 25000
8.8%
i 20829
7.4%
S 20806
7.4%
g 14060
 
5.0%
a 12533
 
4.4%
r 12500
 
4.4%
h 12500
 
4.4%
Other values (13) 44593
15.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 155264
54.9%
Lowercase Letter 119398
42.2%
Space Separator 8306
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 25000
20.9%
i 20829
17.4%
g 14060
11.8%
a 12533
10.5%
r 12500
10.5%
h 12500
10.5%
e 9866
 
8.3%
m 8306
 
7.0%
l 1616
 
1.4%
n 1583
 
1.3%
Other values (3) 605
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
A 41089
26.5%
P 39529
25.5%
T 39529
25.5%
S 20806
13.4%
U 10109
 
6.5%
V 3620
 
2.3%
N 549
 
0.4%
L 23
 
< 0.1%
C 10
 
< 0.1%
Space Separator
ValueCountFrequency (%)
8306
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 274662
97.1%
Common 8306
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 41089
15.0%
P 39529
14.4%
T 39529
14.4%
t 25000
9.1%
i 20829
7.6%
S 20806
7.6%
g 14060
 
5.1%
a 12533
 
4.6%
r 12500
 
4.6%
h 12500
 
4.6%
Other values (12) 36287
13.2%
Common
ValueCountFrequency (%)
8306
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 282968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 41089
14.5%
P 39529
14.0%
T 39529
14.0%
t 25000
8.8%
i 20829
7.4%
S 20806
7.4%
g 14060
 
5.0%
a 12533
 
4.4%
r 12500
 
4.4%
h 12500
 
4.4%
Other values (13) 44593
15.8%

Travel_Controls
Categorical

Distinct6
Distinct (%)0.1%
Missing363493
Missing (%)97.5%
Memory size13.8 MiB
Differential Steer
4998 
Finger Tip
2641 
2 Pedal
847 
Lever
524 
Pedal
 
403

Length

Max length18
Median length18
Mean length13.479198
Min length5

Characters and Unicode

Total characters127001
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLever
2nd row2 Pedal
3rd row2 Pedal
4th row2 Pedal
5th row2 Pedal

Common Values

ValueCountFrequency (%)
Differential Steer 4998
 
1.3%
Finger Tip 2641
 
0.7%
2 Pedal 847
 
0.2%
Lever 524
 
0.1%
Pedal 403
 
0.1%
1 Speed 9
 
< 0.1%
(Missing) 363493
97.5%

Length

2023-06-25T19:54:39.601526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:39.728357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
differential 4998
27.9%
steer 4998
27.9%
finger 2641
14.7%
tip 2641
14.7%
pedal 1250
 
7.0%
2 847
 
4.7%
lever 524
 
2.9%
1 9
 
0.1%
speed 9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 24949
19.6%
i 15278
12.0%
r 13161
10.4%
f 9996
7.9%
t 9996
7.9%
8495
 
6.7%
n 7639
 
6.0%
a 6248
 
4.9%
l 6248
 
4.9%
S 5007
 
3.9%
Other values (11) 19984
15.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100589
79.2%
Uppercase Letter 17061
 
13.4%
Space Separator 8495
 
6.7%
Decimal Number 856
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 24949
24.8%
i 15278
15.2%
r 13161
13.1%
f 9996
9.9%
t 9996
9.9%
n 7639
 
7.6%
a 6248
 
6.2%
l 6248
 
6.2%
p 2650
 
2.6%
g 2641
 
2.6%
Other values (2) 1783
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
S 5007
29.3%
D 4998
29.3%
F 2641
15.5%
T 2641
15.5%
P 1250
 
7.3%
L 524
 
3.1%
Decimal Number
ValueCountFrequency (%)
2 847
98.9%
1 9
 
1.1%
Space Separator
ValueCountFrequency (%)
8495
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 117650
92.6%
Common 9351
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 24949
21.2%
i 15278
13.0%
r 13161
11.2%
f 9996
8.5%
t 9996
8.5%
n 7639
 
6.5%
a 6248
 
5.3%
l 6248
 
5.3%
S 5007
 
4.3%
D 4998
 
4.2%
Other values (8) 14130
12.0%
Common
ValueCountFrequency (%)
8495
90.8%
2 847
 
9.1%
1 9
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 24949
19.6%
i 15278
12.0%
r 13161
10.4%
f 9996
7.9%
t 9996
7.9%
8495
 
6.7%
n 7639
 
6.0%
a 6248
 
4.9%
l 6248
 
4.9%
S 5007
 
3.9%
Other values (11) 19984
15.7%

Differential_Type
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing308097
Missing (%)82.6%
Memory size13.8 MiB
Standard
63467 
Limited Slip
 
1142
No Spin
 
207
Locking
 
2

Length

Max length12
Median length8
Mean length8.0672498
Min length7

Characters and Unicode

Total characters522903
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard
2nd rowStandard
3rd rowStandard
4th rowStandard
5th rowStandard

Common Values

ValueCountFrequency (%)
Standard 63467
 
17.0%
Limited Slip 1142
 
0.3%
No Spin 207
 
0.1%
Locking 2
 
< 0.1%
(Missing) 308097
82.6%

Length

2023-06-25T19:54:39.833141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:39.926270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
standard 63467
95.9%
limited 1142
 
1.7%
slip 1142
 
1.7%
no 207
 
0.3%
spin 207
 
0.3%
locking 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
d 128076
24.5%
a 126934
24.3%
S 64816
12.4%
t 64609
12.4%
n 63676
12.2%
r 63467
12.1%
i 3635
 
0.7%
p 1349
 
0.3%
1349
 
0.3%
L 1144
 
0.2%
Other values (8) 3848
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 455387
87.1%
Uppercase Letter 66167
 
12.7%
Space Separator 1349
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 128076
28.1%
a 126934
27.9%
t 64609
14.2%
n 63676
14.0%
r 63467
13.9%
i 3635
 
0.8%
p 1349
 
0.3%
l 1142
 
0.3%
e 1142
 
0.3%
m 1142
 
0.3%
Other values (4) 215
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
S 64816
98.0%
L 1144
 
1.7%
N 207
 
0.3%
Space Separator
ValueCountFrequency (%)
1349
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 521554
99.7%
Common 1349
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 128076
24.6%
a 126934
24.3%
S 64816
12.4%
t 64609
12.4%
n 63676
12.2%
r 63467
12.2%
i 3635
 
0.7%
p 1349
 
0.3%
L 1144
 
0.2%
l 1142
 
0.2%
Other values (7) 2706
 
0.5%
Common
ValueCountFrequency (%)
1349
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 522903
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 128076
24.5%
a 126934
24.3%
S 64816
12.4%
t 64609
12.4%
n 63676
12.2%
r 63467
12.1%
i 3635
 
0.7%
p 1349
 
0.3%
1349
 
0.3%
L 1144
 
0.2%
Other values (8) 3848
 
0.7%

Steering_Controls
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing308133
Missing (%)82.6%
Memory size13.8 MiB
Conventional
64081 
Command Control
 
572
Four Wheel Standard
 
118
Wheel
 
10
No
 
1

Length

Max length19
Median length12
Mean length12.038004
Min length2

Characters and Unicode

Total characters779846
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowConventional
2nd rowConventional
3rd rowConventional
4th rowConventional
5th rowConventional

Common Values

ValueCountFrequency (%)
Conventional 64081
 
17.2%
Command Control 572
 
0.2%
Four Wheel Standard 118
 
< 0.1%
Wheel 10
 
< 0.1%
No 1
 
< 0.1%
(Missing) 308133
82.6%

Length

2023-06-25T19:54:40.009981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-25T19:54:40.109456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
conventional 64081
97.7%
command 572
 
0.9%
control 572
 
0.9%
wheel 128
 
0.2%
four 118
 
0.2%
standard 118
 
0.2%
no 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 193505
24.8%
o 129997
16.7%
C 65225
 
8.4%
a 64889
 
8.3%
l 64781
 
8.3%
t 64771
 
8.3%
e 64337
 
8.2%
v 64081
 
8.2%
i 64081
 
8.2%
m 1144
 
0.1%
Other values (9) 3035
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 713448
91.5%
Uppercase Letter 65590
 
8.4%
Space Separator 808
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 193505
27.1%
o 129997
18.2%
a 64889
 
9.1%
l 64781
 
9.1%
t 64771
 
9.1%
e 64337
 
9.0%
v 64081
 
9.0%
i 64081
 
9.0%
m 1144
 
0.2%
d 808
 
0.1%
Other values (3) 1054
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
C 65225
99.4%
W 128
 
0.2%
F 118
 
0.2%
S 118
 
0.2%
N 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
808
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 779038
99.9%
Common 808
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 193505
24.8%
o 129997
16.7%
C 65225
 
8.4%
a 64889
 
8.3%
l 64781
 
8.3%
t 64771
 
8.3%
e 64337
 
8.3%
v 64081
 
8.2%
i 64081
 
8.2%
m 1144
 
0.1%
Other values (8) 2227
 
0.3%
Common
ValueCountFrequency (%)
808
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 779846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 193505
24.8%
o 129997
16.7%
C 65225
 
8.4%
a 64889
 
8.3%
l 64781
 
8.3%
t 64771
 
8.3%
e 64337
 
8.2%
v 64081
 
8.2%
i 64081
 
8.2%
m 1144
 
0.1%
Other values (9) 3035
 
0.4%

saleYear
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.1991
Minimum1989
Maximum2012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 MiB
2023-06-25T19:54:40.198886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1989
5-th percentile1992
Q12000
median2006
Q32009
95-th percentile2011
Maximum2012
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.9089345
Coefficient of variation (CV)0.0029482772
Kurtosis-0.32923359
Mean2004.1991
Median Absolute Deviation (MAD)4
Skewness-0.7868436
Sum7.473959 × 108
Variance34.915507
MonotonicityIncreasing
2023-06-25T19:54:40.289368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2009 38247
 
10.3%
2008 36332
 
9.7%
2011 32516
 
8.7%
2007 28991
 
7.8%
2010 28329
 
7.6%
2006 19696
 
5.3%
2005 18100
 
4.9%
2004 18046
 
4.8%
2001 16124
 
4.3%
2002 16062
 
4.3%
Other values (14) 120472
32.3%
ValueCountFrequency (%)
1989 4805
1.3%
1990 4529
 
1.2%
1991 5108
1.4%
1992 5519
1.5%
1993 6303
1.7%
1994 7343
2.0%
1995 7686
2.1%
1996 8179
2.2%
1997 8886
2.4%
1998 11663
3.1%
ValueCountFrequency (%)
2012 10367
 
2.8%
2011 32516
8.7%
2010 28329
7.6%
2009 38247
10.3%
2008 36332
9.7%
2007 28991
7.8%
2006 19696
5.3%
2005 18100
4.9%
2004 18046
4.8%
2003 14016
 
3.8%

saleMonth
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3219018
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 MiB
2023-06-25T19:54:40.379296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4441863
Coefficient of variation (CV)0.54480225
Kurtosis-1.2980288
Mean6.3219018
Median Absolute Deviation (MAD)3
Skewness0.21615651
Sum2357532
Variance11.862419
MonotonicityNot monotonic
2023-06-25T19:54:40.460662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 53688
14.4%
2 50824
13.6%
6 42288
11.3%
9 39320
10.5%
12 36311
9.7%
5 28259
7.6%
4 27319
7.3%
10 27075
7.3%
11 23621
6.3%
8 20402
 
5.5%
Other values (2) 23808
6.4%
ValueCountFrequency (%)
1 9924
 
2.7%
2 50824
13.6%
3 53688
14.4%
4 27319
7.3%
5 28259
7.6%
6 42288
11.3%
7 13884
 
3.7%
8 20402
 
5.5%
9 39320
10.5%
10 27075
7.3%
ValueCountFrequency (%)
12 36311
9.7%
11 23621
6.3%
10 27075
7.3%
9 39320
10.5%
8 20402
 
5.5%
7 13884
 
3.7%
6 42288
11.3%
5 28259
7.6%
4 27319
7.3%
3 53688
14.4%

saleDate
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.121481
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 MiB
2023-06-25T19:54:40.569078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.4435263
Coefficient of variation (CV)0.52374384
Kurtosis-1.0969815
Mean16.121481
Median Absolute Deviation (MAD)7
Skewness-0.022644949
Sum6011942
Variance71.293136
MonotonicityNot monotonic
2023-06-25T19:54:40.713452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
15 16897
 
4.5%
16 16786
 
4.5%
20 14171
 
3.8%
18 14073
 
3.8%
12 13821
 
3.7%
26 13567
 
3.6%
19 13425
 
3.6%
9 13179
 
3.5%
17 12942
 
3.5%
13 12877
 
3.5%
Other values (21) 231177
62.0%
ValueCountFrequency (%)
1 9496
2.5%
2 9446
2.5%
3 8293
2.2%
4 11891
3.2%
5 10631
2.9%
6 12014
3.2%
7 11862
3.2%
8 11593
3.1%
9 13179
3.5%
10 11059
3.0%
ValueCountFrequency (%)
31 6025
1.6%
30 10357
2.8%
29 12517
3.4%
28 12757
3.4%
27 12468
3.3%
26 13567
3.6%
25 11332
3.0%
24 12120
3.3%
23 11002
3.0%
22 11052
3.0%

saleDayOfWeek
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5730636
Minimum0
Maximum6
Zeros22103
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size13.8 MiB
2023-06-25T19:54:40.806859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4143274
Coefficient of variation (CV)0.5496667
Kurtosis-0.52289016
Mean2.5730636
Median Absolute Deviation (MAD)1
Skewness0.21883444
Sum959534
Variance2.0003219
MonotonicityNot monotonic
2023-06-25T19:54:40.881080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 111315
29.8%
2 83688
22.4%
1 71619
19.2%
4 39925
 
10.7%
5 38696
 
10.4%
0 22103
 
5.9%
6 5569
 
1.5%
ValueCountFrequency (%)
0 22103
 
5.9%
1 71619
19.2%
2 83688
22.4%
3 111315
29.8%
4 39925
 
10.7%
5 38696
 
10.4%
6 5569
 
1.5%
ValueCountFrequency (%)
6 5569
 
1.5%
5 38696
 
10.4%
4 39925
 
10.7%
3 111315
29.8%
2 83688
22.4%
1 71619
19.2%
0 22103
 
5.9%

saleDayOfYear
Real number (ℝ)

Distinct360
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.41476
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 MiB
2023-06-25T19:54:40.992329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36
Q182
median166
Q3270
95-th percentile344
Maximum365
Range364
Interquartile range (IQR)188

Descriptive statistics

Standard deviation104.10706
Coefficient of variation (CV)0.58680046
Kurtosis-1.3396677
Mean177.41476
Median Absolute Deviation (MAD)94
Skewness0.18468481
Sum66160625
Variance10838.28
MonotonicityNot monotonic
2023-06-25T19:54:41.119716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 3579
 
1.0%
40 3238
 
0.9%
88 3224
 
0.9%
85 3210
 
0.9%
35 3113
 
0.8%
89 3070
 
0.8%
86 2783
 
0.7%
39 2767
 
0.7%
44 2758
 
0.7%
47 2690
 
0.7%
Other values (350) 342483
91.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1
 
< 0.1%
3 2
 
< 0.1%
4 7
 
< 0.1%
5 9
 
< 0.1%
6 27
 
< 0.1%
7 94
< 0.1%
8 50
< 0.1%
9 30
 
< 0.1%
10 92
< 0.1%
ValueCountFrequency (%)
365 167
 
< 0.1%
364 37
 
< 0.1%
363 266
 
0.1%
362 574
0.2%
361 34
 
< 0.1%
357 3
 
< 0.1%
356 89
 
< 0.1%
355 46
 
< 0.1%
354 770
0.2%
353 1045
0.3%

Interactions

2023-06-25T19:54:13.278020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:41.100687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:43.718268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:46.362649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:48.874051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:51.361050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:53.862330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:58.431936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:00.970819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:03.111140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:05.543499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:08.268672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:10.769614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:13.457466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:41.347479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:43.915380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:46.572156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:49.057649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:51.547539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:54.052305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:58.622582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:01.127275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:03.299641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:05.732927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:08.473390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:10.952790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:13.650140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:41.571183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:44.117657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:46.781978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:49.240920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:51.727358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:54.249391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:58.853377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:01.289530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:03.490984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:05.949112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:08.679529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:11.163972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:13.833497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:41.773978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:44.315895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:46.975212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:49.479997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:51.901471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:54.436610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:59.040590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:01.447189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:03.687333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:06.127222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:08.847574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:11.352274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:14.012552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:41.967364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:44.511594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:47.163211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:49.673106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:52.081191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:54.628684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:59.262877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:01.617698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:03.883013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:06.306589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:09.025123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:11.560138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:14.194478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:42.170476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:44.741192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:47.362378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:49.867462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:52.267577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:54.802627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:59.452496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:01.775115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:04.072041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:06.520118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:09.215242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:11.747259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:14.369918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:42.375675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:44.956868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:47.557871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:50.059466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:52.455195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:54.993676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:59.648611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:01.941568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:04.265637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:06.932442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:09.403861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:11.960922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:14.526786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:42.541001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:45.128759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:47.707141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:50.217931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:52.628295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:55.142533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:59.808041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:02.101124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:04.420507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:07.114917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:09.572182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:12.110777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:14.718881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:42.768364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:45.332213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:47.915532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:50.434323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:52.851868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:57.438975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:00.032376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:02.268206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:04.608370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:07.324306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:09.799194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:12.303295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:14.894331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:42.960616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:45.526745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:48.105125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:50.619859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:53.047308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:57.628597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:00.228728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:02.417423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:04.816749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:07.492180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:09.977838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:12.479966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:15.073995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:43.145265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:45.722544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:48.296988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:50.803074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:53.257282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:57.808217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:00.434536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:02.579964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:04.990659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:07.702305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:10.144515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:12.675386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:15.249675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:43.333012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:45.925003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:48.498414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:50.986000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:53.457118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:58.021546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:00.620962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:02.743332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:05.180569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:07.908924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:10.317309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:12.889897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:15.416703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:43.519319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:46.127797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:48.688730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:51.170966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:53.653590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:53:58.227287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:00.812101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:02.898879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:05.367446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:08.091620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:10.556396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-25T19:54:13.093032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Missing values

2023-06-25T19:54:17.224070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-25T19:54:20.590050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-25T19:54:29.558182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SalesIDSalePriceMachineIDModelIDdatasourceauctioneerIDYearMadeMachineHoursCurrentMeterUsageBandfiModelDescfiBaseModelfiSecondaryDescfiModelSeriesfiModelDescriptorProductSizefiProductClassDescstateProductGroupProductGroupDescDrive_SystemEnclosureForksPad_TypeRide_ControlStickTransmissionTurbochargedBlade_ExtensionBlade_WidthEnclosure_TypeEngine_HorsepowerHydraulicsPushblockRipperScarifierTip_ControlTire_SizeCouplerCoupler_SystemGrouser_TracksHydraulics_FlowTrack_TypeUndercarriage_Pad_WidthStick_LengthThumbPattern_ChangerGrouser_TypeBackhoe_MountingBlade_TypeTravel_ControlsDifferential_TypeSteering_ControlssaleYearsaleMonthsaleDatesaleDayOfWeeksaleDayOfYear
016467709500.01126363843413218.01974NaNNaNTD20TD20NaNNaNNaNMediumTrack Type Tractor, Dozer - 105.0 to 130.0 HorsepowerTexasTTTTrack Type TractorsNaNOROPSNaNNaNNaNNaNDirect DriveNaNNaNNaNNaNNaN2 ValveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStraightNaNNaNNaN1989117117
1182151414000.011940891015013299.01980NaNNaNA66A66NaNNaNNaNNaNWheel Loader - 120.0 to 135.0 HorsepowerFloridaWLWheel LoaderNaNOROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2 ValveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardConventional1989131131
2150513850000.01473654413913299.01978NaNNaND7GD7GNaNNaNLargeTrack Type Tractor, Dozer - 190.0 to 260.0 HorsepowerFloridaTTTTrack Type TractorsNaNOROPSNaNNaNNaNNaNStandardNaNNaNNaNNaNNaN2 ValveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStraightNaNNaNNaN1989131131
3167117416000.01327630859113299.01980NaNNaNA62A62NaNNaNNaNNaNWheel Loader - UnidentifiedFloridaWLWheel LoaderNaNEROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2 ValveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardConventional1989131131
4132905622000.01336053408913299.01984NaNNaND3BD3BNaNNaNNaNTrack Type Tractor, Dozer - 20.0 to 75.0 HorsepowerFloridaTTTTrack Type TractorsNaNOROPSNaNNaNNaNNaNStandardNaNNaNNaNNaNNaN2 ValveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPATLeverNaNNaN1989131131
5130188423500.01182999412313299.01976NaNNaND6CD6CNaNNaNMediumTrack Type Tractor, Dozer - 130.0 to 160.0 HorsepowerFloridaTTTTrack Type TractorsNaNOROPSNaNNaNNaNNaNStandardNaNNaNNaNNaNNaN2 ValveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStraightNaNNaNNaN1989131131
6137922831000.01082797762013299.01986NaNNaNIT12IT12NaNNaNNaNCompactWheel Loader - 60.0 to 80.0 HorsepowerFloridaWLWheel LoaderNaNEROPSYesNaNNaNNaNNaNNaNNaNNaNNaNNaN2 ValveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardConventional1989131131
7164539011750.01527216820213299.01970NaNNaN544544NaNNaNNaNNaNWheel Loader - 90.0 to 100.0 HorsepowerFloridaWLWheel LoaderNaNOROPSYesNaNNaNNaNNaNNaNNaNNaNNaNNaN2 ValveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardConventional1989131131
8149327963000.01363756275913299.01987NaNNaND5HIID5HIINaNMediumTrack Type Tractor, Dozer - 130.0 to 160.0 HorsepowerFloridaTTTTrack Type TractorsNaNEROPSNaNNaNNaNNaNPowershiftNaNNaNNaNNaNNaN2 ValveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPATNaNNaNNaN1989131131
9144954913000.01289412335613299.01966NaNNaN12F12FNaNNaNNaNMotorgrader - 45.0 to 130.0 HorsepowerFloridaMGMotor GradersNoOROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNoBase + 1 FunctionNaNNaNYesSideshift & TipNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1989131131
SalesIDSalePriceMachineIDModelIDdatasourceauctioneerIDYearMadeMachineHoursCurrentMeterUsageBandfiModelDescfiBaseModelfiSecondaryDescfiModelSeriesfiModelDescriptorProductSizefiProductClassDescstateProductGroupProductGroupDescDrive_SystemEnclosureForksPad_TypeRide_ControlStickTransmissionTurbochargedBlade_ExtensionBlade_WidthEnclosure_TypeEngine_HorsepowerHydraulicsPushblockRipperScarifierTip_ControlTire_SizeCouplerCoupler_SystemGrouser_TracksHydraulics_FlowTrack_TypeUndercarriage_Pad_WidthStick_LengthThumbPattern_ChangerGrouser_TypeBackhoe_MountingBlade_TypeTravel_ControlsDifferential_TypeSteering_ControlssaleYearsaleMonthsaleDatesaleDayOfWeeksaleDayOfYear
37290563180855500.018736071040214999.02001NaNNaN13001300NaNNaNNaNNaNSkid Steer Loader - 1251.0 to 1351.0 Lb Operating CapacityCaliforniaSSLSkid Steer LoadersNaNOROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAuxiliaryNaNNaNNaNNaNNaNNaNNaNNaNStandardNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN20124285119
372906628363435000.01930870470114999.01998NaNNaN544H544HNaNNaNNaNWheel Loader - 120.0 to 135.0 HorsepowerCaliforniaWLWheel LoaderNaNEROPS w ACNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2 ValveNaNNaNNaNNaN20.5NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardConventional20124285119
37290762823377500.019286882207214999.02005NaNNaN317317NaNNaNNaNNaNSkid Steer Loader - 1601.0 to 1751.0 Lb Operating CapacityCaliforniaSSLSkid Steer LoadersNaNOROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAuxiliaryNaNNaNNaNNaNNaNNaNNaNNaNStandardNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN20124285119
372908631132113000.018238461731114999.02005NaNNaNS175S175NaNNaNNaNNaNSkid Steer Loader - 1601.0 to 1751.0 Lb Operating CapacityCaliforniaSSLSkid Steer LoadersNaNOROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAuxiliaryNaNNaNNaNNaNNaNNaNNaNNaNStandardNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN20124285119
372909630647653000.01278794911414999.02004NaNNaNWA320WA320NaNNaNNaNMediumWheel Loader - 150.0 to 175.0 HorsepowerCaliforniaWLWheel LoaderNaNEROPS w ACNaNNaNYesNaNNaNNaNNaNNaNNaNNaN2 ValveNaNNaNNaNNaN20.5NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNStandardConventional20124285119
372910628121939000.017920491397614999.02000NaNNaN710VHP710NaNNaNVHPNaNMotorgrader - 130.0 to 145.0 HorsepowerCaliforniaMGMotor GradersNoEROPS w ACNaNNaNNaNNaNNaNNaNNaN12'NaNNoBase + 1 FunctionNaNYesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN20124285119
372911630298416000.01915521526614999.02001NaNNaND38ED38ENaNNaNNaNTrack Type Tractor, Dozer - 75.0 to 85.0 HorsepowerCaliforniaTTTTrack Type TractorsNaNOROPSNaNNaNNaNNaNStandardNaNNaNNaNNaNNaN2 ValveNaNMulti ShankNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPATNaNNaNNaN20124285119
37291263248116000.019191041933014999.02004NaNNaN20642064NaNNaNNaNNaNSkid Steer Loader - 1751.0 to 2201.0 Lb Operating CapacityCaliforniaSSLSkid Steer LoadersNaNOROPSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAuxiliaryNaNNaNNaNNaNNaNNaNNaNNaNStandardNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN20124285119
372913631302916000.019184161724414999.02004NaNNaN337G337GNaNNaNMiniHydraulic Excavator, Track - 5.0 to 6.0 Metric TonsCaliforniaTEXTrack ExcavatorsNaNEROPS w ACNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAuxiliaryNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN20124285119
372914626625155000.0509560335714999.01993NaNNaN12G12GNaNNaNNaNMotorgrader - 130.0 to 145.0 HorsepowerCaliforniaMGMotor GradersNoOROPSNaNNaNNaNNaNNaNNaNNaN14'NaNNoBase + 1 FunctionYesYesYesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN20124285119